{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b164b8fb7d183a33",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "# 1. 加载数据集 MNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9f521e7e909639ea",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-04T02:14:31.344927Z",
     "start_time": "2024-03-04T02:14:29.286878Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "from torchvision import datasets, transforms\n",
    "\n",
    "transform = transforms.Compose([\n",
    "    transforms.ToTensor(), # value of pixel: [0, 255] -> [0, 1]\n",
    "    transforms.Normalize(mean = (0.5,), std = (0.5,)) # value of tensor: [0, 1] -> [-1, 1]\n",
    "])\n",
    "mnist = datasets.MNIST(root='data', train=True, download=True, transform=transform)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac21521366850286",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "`transforms.Normalize()`用于将图像进行标准化：$\\rm{\\frac{(x - mean)}{std}}$，使得处理的数据呈正态分布。\n",
    "\n",
    "由于 MNIST 数据集图像为灰度图只有一个通道，因此只需要设置单个通道的 mean 与 std 即可。\n",
    "\n",
    "这里的取值，可以是将图像像素值[0,255] 缩放至 [0, 1]后求得均值和方差，也可以是根据经验设置，即 mean=0.5, std=0.5。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b073013dd5b18576",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "# 2. 查看数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8dc32eb93533f33f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-04T02:14:31.353103Z",
     "start_time": "2024-03-04T02:14:31.345932Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label: 3\n",
      "Some pixel values: tensor([[-0.9451, -0.6392, -0.9843, -1.0000, -1.0000],\n",
      "        [-1.0000, -1.0000, -1.0000, -0.9529, -0.7725],\n",
      "        [-1.0000, -0.8745, -0.0196,  0.5765,  0.7725],\n",
      "        [-1.0000,  0.0902,  0.9922,  0.9922,  0.9922],\n",
      "        [-1.0000, -0.3569,  0.1216,  0.1216, -0.5686]])\n",
      "Min value: -1.0, Max value: 1.0\n"
     ]
    }
   ],
   "source": [
    "img, label = mnist[len(mnist)-500]\n",
    "print(f\"Label: {label}\")\n",
    "print(f\"Some pixel values: {img[0, 10:15, 10:15]}\")\n",
    "print(f\"Min value: {img.min()}, Max value: {img.max()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5abcafa0721af439",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-04T02:14:31.776857Z",
     "start_time": "2024-03-04T02:14:31.354115Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x187d76c7990>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "def dnorm(x:torch.Tensor):\n",
    "    min_value = -1\n",
    "    max_value = 1\n",
    "    out = (x - min_value) / (max_value - min_value)\n",
    "    return out.clamp(0,1)   # plt expects values in [0,1]\n",
    "\n",
    "img_norm = dnorm(img)   # shape: (1, 28, 28)\n",
    "plt.imshow(img_norm.squeeze(0), cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c13a6bddea6a50c",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "# 3. 制作数据加载器Dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a04f59d4864379c6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-04T02:14:31.780392Z",
     "start_time": "2024-03-04T02:14:31.776857Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "batch_size = 100\n",
    "data_loader = DataLoader(mnist, batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20b940c0eff3b179",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "# 4. 创建GAN的生成器与判别器并测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7fe792b4c4b0a39a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-04T02:14:31.789044Z",
     "start_time": "2024-03-04T02:14:31.782398Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "from model import Generator, Discriminator\n",
    "\n",
    "image_size = 28 * 28\n",
    "hidden_size = 256\n",
    "latent_size = 64\n",
    "\n",
    "G = Generator(image_size=image_size, hidden_size=hidden_size, latent_size=latent_size)\n",
    "D = Discriminator(image_size=image_size, hidden_size=hidden_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "77a1dd0dc11b83df",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-04T02:14:31.897152Z",
     "start_time": "2024-03-04T02:14:31.790050Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Result from Discriminator: 0.5166\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x187d76fc210>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "untrained_G_out = G(torch.randn(latent_size))  # Shape: [latent_size]\n",
    "untrained_D_out = D(untrained_G_out.view(1, -1))\n",
    "print(f\"Result from Discriminator: {untrained_D_out.item():.4f}\")\n",
    "plt.imshow(untrained_G_out.view(28, 28).detach(), cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c75d67a8e037dc36",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "# 5. 对抗训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "293340f50a3ed8a7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-04T02:14:32.006331Z",
     "start_time": "2024-03-04T02:14:31.898157Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "from torch import optim\n",
    "from torch import nn\n",
    "num_epochs = 300\n",
    "device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n",
    "D.to(device=device)\n",
    "G.to(device=device)\n",
    "\n",
    "d_optim = optim.Adam(D.parameters(), lr=0.002)\n",
    "g_optim = optim.Adam(G.parameters(), lr=0.002)\n",
    "\n",
    "criterion = nn.BCELoss()\n",
    "\n",
    "d_loss_list, g_loss_list, real_score_list, fake_score_list = ([] for _ in range(4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9aeaf425f848c0e8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-04T03:14:18.872783Z",
     "start_time": "2024-03-04T02:14:32.006331Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: [1/300], Batch: [300/600]Discriminator Loss: 1.1440, Generator Loss: 0.5215\n",
      "Epoch: [1/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8644\n",
      "Epoch: [1/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6283\n",
      "Epoch: [1/300], Batch: [600/600]Discriminator Loss: 1.3556, Generator Loss: 0.8904\n",
      "Epoch: [1/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.9466\n",
      "Epoch: [1/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6932\n",
      "Epoch: [2/300], Batch: [300/600]Discriminator Loss: 1.3900, Generator Loss: 0.6361\n",
      "Epoch: [2/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.9253\n",
      "Epoch: [2/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.7096\n",
      "Epoch: [2/300], Batch: [600/600]Discriminator Loss: 1.0087, Generator Loss: 0.6839\n",
      "Epoch: [2/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8953\n",
      "Epoch: [2/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5827\n",
      "Epoch: [3/300], Batch: [300/600]Discriminator Loss: 1.2533, Generator Loss: 0.6790\n",
      "Epoch: [3/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.9096\n",
      "Epoch: [3/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6790\n",
      "Epoch: [3/300], Batch: [600/600]Discriminator Loss: 1.1900, Generator Loss: 0.6982\n",
      "Epoch: [3/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.9132\n",
      "Epoch: [3/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6563\n",
      "Epoch: [4/300], Batch: [300/600]Discriminator Loss: 0.9792, Generator Loss: 0.6476\n",
      "Epoch: [4/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8735\n",
      "Epoch: [4/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5526\n",
      "Epoch: [4/300], Batch: [600/600]Discriminator Loss: 0.9004, Generator Loss: 0.5908\n",
      "Epoch: [4/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.9352\n",
      "Epoch: [4/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5580\n",
      "Epoch: [5/300], Batch: [300/600]Discriminator Loss: 0.7816, Generator Loss: 0.6551\n",
      "Epoch: [5/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.9580\n",
      "Epoch: [5/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5100\n",
      "Epoch: [5/300], Batch: [600/600]Discriminator Loss: 0.9206, Generator Loss: 0.6526\n",
      "Epoch: [5/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.9233\n",
      "Epoch: [5/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5489\n",
      "Epoch: [6/300], Batch: [300/600]Discriminator Loss: 0.9081, Generator Loss: 0.6479\n",
      "Epoch: [6/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.9205\n",
      "Epoch: [6/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5359\n",
      "Epoch: [6/300], Batch: [600/600]Discriminator Loss: 0.8317, Generator Loss: 0.5926\n",
      "Epoch: [6/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.9052\n",
      "Epoch: [6/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5036\n",
      "Epoch: [7/300], Batch: [300/600]Discriminator Loss: 0.9293, Generator Loss: 0.5517\n",
      "Epoch: [7/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8801\n",
      "Epoch: [7/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5268\n",
      "Epoch: [7/300], Batch: [600/600]Discriminator Loss: 1.0971, Generator Loss: 0.8050\n",
      "Epoch: [7/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.9426\n",
      "Epoch: [7/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6130\n",
      "Epoch: [8/300], Batch: [300/600]Discriminator Loss: 0.9223, Generator Loss: 0.6443\n",
      "Epoch: [8/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8875\n",
      "Epoch: [8/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5346\n",
      "Epoch: [8/300], Batch: [600/600]Discriminator Loss: 0.8996, Generator Loss: 0.6848\n",
      "Epoch: [8/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8917\n",
      "Epoch: [8/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5241\n",
      "Epoch: [9/300], Batch: [300/600]Discriminator Loss: 1.0371, Generator Loss: 0.5600\n",
      "Epoch: [9/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.9413\n",
      "Epoch: [9/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6035\n",
      "Epoch: [9/300], Batch: [600/600]Discriminator Loss: 1.0384, Generator Loss: 0.4981\n",
      "Epoch: [9/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.9104\n",
      "Epoch: [9/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5958\n",
      "Epoch: [10/300], Batch: [300/600]Discriminator Loss: 0.9348, Generator Loss: 0.7531\n",
      "Epoch: [10/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.9003\n",
      "Epoch: [10/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5443\n",
      "Epoch: [10/300], Batch: [600/600]Discriminator Loss: 1.0004, Generator Loss: 0.5797\n",
      "Epoch: [10/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8548\n",
      "Epoch: [10/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5513\n",
      "Generated images at epoch 10\n",
      "Epoch: [11/300], Batch: [300/600]Discriminator Loss: 1.1735, Generator Loss: 0.6448\n",
      "Epoch: [11/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8757\n",
      "Epoch: [11/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5927\n",
      "Epoch: [11/300], Batch: [600/600]Discriminator Loss: 0.8900, Generator Loss: 0.6472\n",
      "Epoch: [11/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8820\n",
      "Epoch: [11/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.4975\n",
      "Epoch: [12/300], Batch: [300/600]Discriminator Loss: 1.0949, Generator Loss: 0.5862\n",
      "Epoch: [12/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8762\n",
      "Epoch: [12/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5936\n",
      "Epoch: [12/300], Batch: [600/600]Discriminator Loss: 1.0737, Generator Loss: 0.4064\n",
      "Epoch: [12/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8850\n",
      "Epoch: [12/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5975\n",
      "Epoch: [13/300], Batch: [300/600]Discriminator Loss: 1.0460, Generator Loss: 0.5907\n",
      "Epoch: [13/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8333\n",
      "Epoch: [13/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5552\n",
      "Epoch: [13/300], Batch: [600/600]Discriminator Loss: 1.1847, Generator Loss: 0.5576\n",
      "Epoch: [13/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8800\n",
      "Epoch: [13/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6373\n",
      "Epoch: [14/300], Batch: [300/600]Discriminator Loss: 1.1847, Generator Loss: 0.7141\n",
      "Epoch: [14/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8580\n",
      "Epoch: [14/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6216\n",
      "Epoch: [14/300], Batch: [600/600]Discriminator Loss: 1.0674, Generator Loss: 0.5115\n",
      "Epoch: [14/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8291\n",
      "Epoch: [14/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5656\n",
      "Epoch: [15/300], Batch: [300/600]Discriminator Loss: 1.0755, Generator Loss: 0.4573\n",
      "Epoch: [15/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8142\n",
      "Epoch: [15/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5671\n",
      "Epoch: [15/300], Batch: [600/600]Discriminator Loss: 1.1206, Generator Loss: 0.7002\n",
      "Epoch: [15/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8090\n",
      "Epoch: [15/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5571\n",
      "Epoch: [16/300], Batch: [300/600]Discriminator Loss: 1.0021, Generator Loss: 0.6806\n",
      "Epoch: [16/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8581\n",
      "Epoch: [16/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5479\n",
      "Epoch: [16/300], Batch: [600/600]Discriminator Loss: 1.2342, Generator Loss: 0.5565\n",
      "Epoch: [16/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8403\n",
      "Epoch: [16/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6189\n",
      "Epoch: [17/300], Batch: [300/600]Discriminator Loss: 1.2547, Generator Loss: 0.5880\n",
      "Epoch: [17/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8152\n",
      "Epoch: [17/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6386\n",
      "Epoch: [17/300], Batch: [600/600]Discriminator Loss: 1.2402, Generator Loss: 0.5218\n",
      "Epoch: [17/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7674\n",
      "Epoch: [17/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6009\n",
      "Epoch: [18/300], Batch: [300/600]Discriminator Loss: 1.2336, Generator Loss: 0.6501\n",
      "Epoch: [18/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8090\n",
      "Epoch: [18/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6163\n",
      "Epoch: [18/300], Batch: [600/600]Discriminator Loss: 1.1047, Generator Loss: 0.6076\n",
      "Epoch: [18/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8141\n",
      "Epoch: [18/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5653\n",
      "Epoch: [19/300], Batch: [300/600]Discriminator Loss: 1.2538, Generator Loss: 0.5193\n",
      "Epoch: [19/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7762\n",
      "Epoch: [19/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6117\n",
      "Epoch: [19/300], Batch: [600/600]Discriminator Loss: 1.1959, Generator Loss: 0.4569\n",
      "Epoch: [19/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7552\n",
      "Epoch: [19/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5809\n",
      "Epoch: [20/300], Batch: [300/600]Discriminator Loss: 1.3158, Generator Loss: 0.5843\n",
      "Epoch: [20/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8307\n",
      "Epoch: [20/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6525\n",
      "Epoch: [20/300], Batch: [600/600]Discriminator Loss: 1.0520, Generator Loss: 0.4900\n",
      "Epoch: [20/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7962\n",
      "Epoch: [20/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5387\n",
      "Generated images at epoch 20\n",
      "Epoch: [21/300], Batch: [300/600]Discriminator Loss: 1.0904, Generator Loss: 0.5518\n",
      "Epoch: [21/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8209\n",
      "Epoch: [21/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5646\n",
      "Epoch: [21/300], Batch: [600/600]Discriminator Loss: 1.2097, Generator Loss: 0.4378\n",
      "Epoch: [21/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7557\n",
      "Epoch: [21/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5822\n",
      "Epoch: [22/300], Batch: [300/600]Discriminator Loss: 1.2151, Generator Loss: 0.6238\n",
      "Epoch: [22/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7798\n",
      "Epoch: [22/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5915\n",
      "Epoch: [22/300], Batch: [600/600]Discriminator Loss: 1.2686, Generator Loss: 0.4319\n",
      "Epoch: [22/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7801\n",
      "Epoch: [22/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6135\n",
      "Epoch: [23/300], Batch: [300/600]Discriminator Loss: 1.1834, Generator Loss: 0.5283\n",
      "Epoch: [23/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7992\n",
      "Epoch: [23/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6010\n",
      "Epoch: [23/300], Batch: [600/600]Discriminator Loss: 1.2158, Generator Loss: 0.5509\n",
      "Epoch: [23/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7726\n",
      "Epoch: [23/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5895\n",
      "Epoch: [24/300], Batch: [300/600]Discriminator Loss: 1.2793, Generator Loss: 0.4273\n",
      "Epoch: [24/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7742\n",
      "Epoch: [24/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6247\n",
      "Epoch: [24/300], Batch: [600/600]Discriminator Loss: 1.2702, Generator Loss: 0.4462\n",
      "Epoch: [24/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7814\n",
      "Epoch: [24/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6231\n",
      "Epoch: [25/300], Batch: [300/600]Discriminator Loss: 1.2179, Generator Loss: 0.5740\n",
      "Epoch: [25/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7470\n",
      "Epoch: [25/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5757\n",
      "Epoch: [25/300], Batch: [600/600]Discriminator Loss: 1.4015, Generator Loss: 0.4578\n",
      "Epoch: [25/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8125\n",
      "Epoch: [25/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6659\n",
      "Epoch: [26/300], Batch: [300/600]Discriminator Loss: 1.2625, Generator Loss: 0.5130\n",
      "Epoch: [26/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7755\n",
      "Epoch: [26/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6068\n",
      "Epoch: [26/300], Batch: [600/600]Discriminator Loss: 1.1683, Generator Loss: 0.4352\n",
      "Epoch: [26/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7322\n",
      "Epoch: [26/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5534\n",
      "Epoch: [27/300], Batch: [300/600]Discriminator Loss: 1.3398, Generator Loss: 0.5072\n",
      "Epoch: [27/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7988\n",
      "Epoch: [27/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6478\n",
      "Epoch: [27/300], Batch: [600/600]Discriminator Loss: 1.3443, Generator Loss: 0.4710\n",
      "Epoch: [27/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7614\n",
      "Epoch: [27/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6363\n",
      "Epoch: [28/300], Batch: [300/600]Discriminator Loss: 1.2936, Generator Loss: 0.5415\n",
      "Epoch: [28/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8055\n",
      "Epoch: [28/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6405\n",
      "Epoch: [28/300], Batch: [600/600]Discriminator Loss: 1.3276, Generator Loss: 0.5500\n",
      "Epoch: [28/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8190\n",
      "Epoch: [28/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6629\n",
      "Epoch: [29/300], Batch: [300/600]Discriminator Loss: 1.2387, Generator Loss: 0.4782\n",
      "Epoch: [29/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7337\n",
      "Epoch: [29/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5878\n",
      "Epoch: [29/300], Batch: [600/600]Discriminator Loss: 1.3124, Generator Loss: 0.3953\n",
      "Epoch: [29/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7388\n",
      "Epoch: [29/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6152\n",
      "Epoch: [30/300], Batch: [300/600]Discriminator Loss: 1.2274, Generator Loss: 0.4712\n",
      "Epoch: [30/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7405\n",
      "Epoch: [30/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5912\n",
      "Epoch: [30/300], Batch: [600/600]Discriminator Loss: 1.3440, Generator Loss: 0.6266\n",
      "Epoch: [30/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7738\n",
      "Epoch: [30/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6445\n",
      "Generated images at epoch 30\n",
      "Epoch: [31/300], Batch: [300/600]Discriminator Loss: 1.4136, Generator Loss: 0.5027\n",
      "Epoch: [31/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7492\n",
      "Epoch: [31/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6529\n",
      "Epoch: [31/300], Batch: [600/600]Discriminator Loss: 1.3773, Generator Loss: 0.5214\n",
      "Epoch: [31/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7400\n",
      "Epoch: [31/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6319\n",
      "Epoch: [32/300], Batch: [300/600]Discriminator Loss: 1.4975, Generator Loss: 0.3760\n",
      "Epoch: [32/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7370\n",
      "Epoch: [32/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6690\n",
      "Epoch: [32/300], Batch: [600/600]Discriminator Loss: 1.3912, Generator Loss: 0.4970\n",
      "Epoch: [32/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7606\n",
      "Epoch: [32/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6530\n",
      "Epoch: [33/300], Batch: [300/600]Discriminator Loss: 1.3740, Generator Loss: 0.6283\n",
      "Epoch: [33/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7601\n",
      "Epoch: [33/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6476\n",
      "Epoch: [33/300], Batch: [600/600]Discriminator Loss: 1.2215, Generator Loss: 0.5475\n",
      "Epoch: [33/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7088\n",
      "Epoch: [33/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5588\n",
      "Epoch: [34/300], Batch: [300/600]Discriminator Loss: 1.3625, Generator Loss: 0.5251\n",
      "Epoch: [34/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7457\n",
      "Epoch: [34/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6351\n",
      "Epoch: [34/300], Batch: [600/600]Discriminator Loss: 1.3437, Generator Loss: 0.5235\n",
      "Epoch: [34/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7834\n",
      "Epoch: [34/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6512\n",
      "Epoch: [35/300], Batch: [300/600]Discriminator Loss: 1.3053, Generator Loss: 0.5669\n",
      "Epoch: [35/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7434\n",
      "Epoch: [35/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6140\n",
      "Epoch: [35/300], Batch: [600/600]Discriminator Loss: 1.3112, Generator Loss: 0.4552\n",
      "Epoch: [35/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7267\n",
      "Epoch: [35/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6088\n",
      "Epoch: [36/300], Batch: [300/600]Discriminator Loss: 1.3142, Generator Loss: 0.4992\n",
      "Epoch: [36/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7890\n",
      "Epoch: [36/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6393\n",
      "Epoch: [36/300], Batch: [600/600]Discriminator Loss: 1.4027, Generator Loss: 0.4786\n",
      "Epoch: [36/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7064\n",
      "Epoch: [36/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6333\n",
      "Epoch: [37/300], Batch: [300/600]Discriminator Loss: 1.2597, Generator Loss: 0.5306\n",
      "Epoch: [37/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.6841\n",
      "Epoch: [37/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5713\n",
      "Epoch: [37/300], Batch: [600/600]Discriminator Loss: 1.3883, Generator Loss: 0.4466\n",
      "Epoch: [37/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8040\n",
      "Epoch: [37/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6667\n",
      "Epoch: [38/300], Batch: [300/600]Discriminator Loss: 1.3793, Generator Loss: 0.4608\n",
      "Epoch: [38/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7679\n",
      "Epoch: [38/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6538\n",
      "Epoch: [38/300], Batch: [600/600]Discriminator Loss: 1.3792, Generator Loss: 0.4626\n",
      "Epoch: [38/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7234\n",
      "Epoch: [38/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6303\n",
      "Epoch: [39/300], Batch: [300/600]Discriminator Loss: 1.1615, Generator Loss: 0.5743\n",
      "Epoch: [39/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7298\n",
      "Epoch: [39/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5486\n",
      "Epoch: [39/300], Batch: [600/600]Discriminator Loss: 1.2967, Generator Loss: 0.3796\n",
      "Epoch: [39/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7247\n",
      "Epoch: [39/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6031\n",
      "Epoch: [40/300], Batch: [300/600]Discriminator Loss: 1.2378, Generator Loss: 0.4468\n",
      "Epoch: [40/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7254\n",
      "Epoch: [40/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5736\n",
      "Epoch: [40/300], Batch: [600/600]Discriminator Loss: 1.3539, Generator Loss: 0.4228\n",
      "Epoch: [40/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7262\n",
      "Epoch: [40/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6262\n",
      "Generated images at epoch 40\n",
      "Epoch: [41/300], Batch: [300/600]Discriminator Loss: 1.2356, Generator Loss: 0.4290\n",
      "Epoch: [41/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7050\n",
      "Epoch: [41/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5671\n",
      "Epoch: [41/300], Batch: [600/600]Discriminator Loss: 1.3657, Generator Loss: 0.5013\n",
      "Epoch: [41/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7590\n",
      "Epoch: [41/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6375\n",
      "Epoch: [42/300], Batch: [300/600]Discriminator Loss: 1.3214, Generator Loss: 0.3803\n",
      "Epoch: [42/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7696\n",
      "Epoch: [42/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6322\n",
      "Epoch: [42/300], Batch: [600/600]Discriminator Loss: 1.2756, Generator Loss: 0.4927\n",
      "Epoch: [42/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7725\n",
      "Epoch: [42/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6182\n",
      "Epoch: [43/300], Batch: [300/600]Discriminator Loss: 1.3936, Generator Loss: 0.5049\n",
      "Epoch: [43/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7875\n",
      "Epoch: [43/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6634\n",
      "Epoch: [43/300], Batch: [600/600]Discriminator Loss: 1.4005, Generator Loss: 0.4976\n",
      "Epoch: [43/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8014\n",
      "Epoch: [43/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6785\n",
      "Epoch: [44/300], Batch: [300/600]Discriminator Loss: 1.1811, Generator Loss: 0.4568\n",
      "Epoch: [44/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.6975\n",
      "Epoch: [44/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5444\n",
      "Epoch: [44/300], Batch: [600/600]Discriminator Loss: 1.2271, Generator Loss: 0.4766\n",
      "Epoch: [44/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7461\n",
      "Epoch: [44/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5820\n",
      "Epoch: [45/300], Batch: [300/600]Discriminator Loss: 1.2388, Generator Loss: 0.4422\n",
      "Epoch: [45/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.6890\n",
      "Epoch: [45/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5619\n",
      "Epoch: [45/300], Batch: [600/600]Discriminator Loss: 1.2667, Generator Loss: 0.5459\n",
      "Epoch: [45/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7298\n",
      "Epoch: [45/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5917\n",
      "Epoch: [46/300], Batch: [300/600]Discriminator Loss: 1.2836, Generator Loss: 0.5634\n",
      "Epoch: [46/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7762\n",
      "Epoch: [46/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6265\n",
      "Epoch: [46/300], Batch: [600/600]Discriminator Loss: 1.4195, Generator Loss: 0.6901\n",
      "Epoch: [46/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7770\n",
      "Epoch: [46/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6637\n",
      "Epoch: [47/300], Batch: [300/600]Discriminator Loss: 1.2262, Generator Loss: 0.5452\n",
      "Epoch: [47/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7663\n",
      "Epoch: [47/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5959\n",
      "Epoch: [47/300], Batch: [600/600]Discriminator Loss: 1.2464, Generator Loss: 0.4487\n",
      "Epoch: [47/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7764\n",
      "Epoch: [47/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6062\n",
      "Epoch: [48/300], Batch: [300/600]Discriminator Loss: 1.4662, Generator Loss: 0.4677\n",
      "Epoch: [48/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7737\n",
      "Epoch: [48/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6807\n",
      "Epoch: [48/300], Batch: [600/600]Discriminator Loss: 1.3040, Generator Loss: 0.5438\n",
      "Epoch: [48/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8019\n",
      "Epoch: [48/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6383\n",
      "Epoch: [49/300], Batch: [300/600]Discriminator Loss: 1.3661, Generator Loss: 0.4746\n",
      "Epoch: [49/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7414\n",
      "Epoch: [49/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6364\n",
      "Epoch: [49/300], Batch: [600/600]Discriminator Loss: 1.3310, Generator Loss: 0.5155\n",
      "Epoch: [49/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7786\n",
      "Epoch: [49/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6413\n",
      "Epoch: [50/300], Batch: [300/600]Discriminator Loss: 1.2315, Generator Loss: 0.5510\n",
      "Epoch: [50/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7168\n",
      "Epoch: [50/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5705\n",
      "Epoch: [50/300], Batch: [600/600]Discriminator Loss: 1.3242, Generator Loss: 0.5232\n",
      "Epoch: [50/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7470\n",
      "Epoch: [50/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6286\n",
      "Generated images at epoch 50\n",
      "Epoch: [51/300], Batch: [300/600]Discriminator Loss: 1.3188, Generator Loss: 0.5198\n",
      "Epoch: [51/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8104\n",
      "Epoch: [51/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6546\n",
      "Epoch: [51/300], Batch: [600/600]Discriminator Loss: 1.2373, Generator Loss: 0.5653\n",
      "Epoch: [51/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7399\n",
      "Epoch: [51/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5947\n",
      "Epoch: [52/300], Batch: [300/600]Discriminator Loss: 1.2511, Generator Loss: 0.4427\n",
      "Epoch: [52/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7409\n",
      "Epoch: [52/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5970\n",
      "Epoch: [52/300], Batch: [600/600]Discriminator Loss: 1.2854, Generator Loss: 0.4030\n",
      "Epoch: [52/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7001\n",
      "Epoch: [52/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5846\n",
      "Epoch: [53/300], Batch: [300/600]Discriminator Loss: 1.3372, Generator Loss: 0.3934\n",
      "Epoch: [53/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7634\n",
      "Epoch: [53/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6419\n",
      "Epoch: [53/300], Batch: [600/600]Discriminator Loss: 1.4082, Generator Loss: 0.5065\n",
      "Epoch: [53/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7640\n",
      "Epoch: [53/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6618\n",
      "Epoch: [54/300], Batch: [300/600]Discriminator Loss: 1.3389, Generator Loss: 0.4184\n",
      "Epoch: [54/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7747\n",
      "Epoch: [54/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6341\n",
      "Epoch: [54/300], Batch: [600/600]Discriminator Loss: 1.3717, Generator Loss: 0.5251\n",
      "Epoch: [54/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7541\n",
      "Epoch: [54/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6401\n",
      "Epoch: [55/300], Batch: [300/600]Discriminator Loss: 1.2653, Generator Loss: 0.4280\n",
      "Epoch: [55/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7674\n",
      "Epoch: [55/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6096\n",
      "Epoch: [55/300], Batch: [600/600]Discriminator Loss: 1.3603, Generator Loss: 0.4895\n",
      "Epoch: [55/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7595\n",
      "Epoch: [55/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6454\n",
      "Epoch: [56/300], Batch: [300/600]Discriminator Loss: 1.2862, Generator Loss: 0.5063\n",
      "Epoch: [56/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7765\n",
      "Epoch: [56/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6223\n",
      "Epoch: [56/300], Batch: [600/600]Discriminator Loss: 1.4066, Generator Loss: 0.4136\n",
      "Epoch: [56/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7878\n",
      "Epoch: [56/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6706\n",
      "Epoch: [57/300], Batch: [300/600]Discriminator Loss: 1.2883, Generator Loss: 0.4463\n",
      "Epoch: [57/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7590\n",
      "Epoch: [57/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6217\n",
      "Epoch: [57/300], Batch: [600/600]Discriminator Loss: 1.2992, Generator Loss: 0.4946\n",
      "Epoch: [57/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7755\n",
      "Epoch: [57/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6311\n",
      "Epoch: [58/300], Batch: [300/600]Discriminator Loss: 1.3433, Generator Loss: 0.5578\n",
      "Epoch: [58/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7847\n",
      "Epoch: [58/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6427\n",
      "Epoch: [58/300], Batch: [600/600]Discriminator Loss: 1.2239, Generator Loss: 0.4712\n",
      "Epoch: [58/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7239\n",
      "Epoch: [58/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5743\n",
      "Epoch: [59/300], Batch: [300/600]Discriminator Loss: 1.3025, Generator Loss: 0.4392\n",
      "Epoch: [59/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7406\n",
      "Epoch: [59/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6113\n",
      "Epoch: [59/300], Batch: [600/600]Discriminator Loss: 1.2194, Generator Loss: 0.4558\n",
      "Epoch: [59/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.6929\n",
      "Epoch: [59/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5504\n",
      "Epoch: [60/300], Batch: [300/600]Discriminator Loss: 1.2783, Generator Loss: 0.4847\n",
      "Epoch: [60/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7698\n",
      "Epoch: [60/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6191\n",
      "Epoch: [60/300], Batch: [600/600]Discriminator Loss: 1.2383, Generator Loss: 0.4649\n",
      "Epoch: [60/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.6995\n",
      "Epoch: [60/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5652\n",
      "Generated images at epoch 60\n",
      "Epoch: [61/300], Batch: [300/600]Discriminator Loss: 1.2431, Generator Loss: 0.5786\n",
      "Epoch: [61/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7706\n",
      "Epoch: [61/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5894\n",
      "Epoch: [61/300], Batch: [600/600]Discriminator Loss: 1.3036, Generator Loss: 0.6224\n",
      "Epoch: [61/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7675\n",
      "Epoch: [61/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6254\n",
      "Epoch: [62/300], Batch: [300/600]Discriminator Loss: 1.2710, Generator Loss: 0.5609\n",
      "Epoch: [62/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7555\n",
      "Epoch: [62/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6053\n",
      "Epoch: [62/300], Batch: [600/600]Discriminator Loss: 1.2333, Generator Loss: 0.5562\n",
      "Epoch: [62/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7535\n",
      "Epoch: [62/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5903\n",
      "Epoch: [63/300], Batch: [300/600]Discriminator Loss: 1.2251, Generator Loss: 0.5764\n",
      "Epoch: [63/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7814\n",
      "Epoch: [63/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5945\n",
      "Epoch: [63/300], Batch: [600/600]Discriminator Loss: 1.3437, Generator Loss: 0.5122\n",
      "Epoch: [63/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7684\n",
      "Epoch: [63/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6246\n",
      "Epoch: [64/300], Batch: [300/600]Discriminator Loss: 1.2796, Generator Loss: 0.4879\n",
      "Epoch: [64/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7316\n",
      "Epoch: [64/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5946\n",
      "Epoch: [64/300], Batch: [600/600]Discriminator Loss: 1.4637, Generator Loss: 0.5630\n",
      "Epoch: [64/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7913\n",
      "Epoch: [64/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6882\n",
      "Epoch: [65/300], Batch: [300/600]Discriminator Loss: 1.2260, Generator Loss: 0.4575\n",
      "Epoch: [65/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7299\n",
      "Epoch: [65/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5766\n",
      "Epoch: [65/300], Batch: [600/600]Discriminator Loss: 1.2905, Generator Loss: 0.4046\n",
      "Epoch: [65/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7735\n",
      "Epoch: [65/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6221\n",
      "Epoch: [66/300], Batch: [300/600]Discriminator Loss: 1.2281, Generator Loss: 0.4375\n",
      "Epoch: [66/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7392\n",
      "Epoch: [66/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5838\n",
      "Epoch: [66/300], Batch: [600/600]Discriminator Loss: 1.2973, Generator Loss: 0.4656\n",
      "Epoch: [66/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7971\n",
      "Epoch: [66/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6311\n",
      "Epoch: [67/300], Batch: [300/600]Discriminator Loss: 1.1737, Generator Loss: 0.4376\n",
      "Epoch: [67/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7473\n",
      "Epoch: [67/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5695\n",
      "Epoch: [67/300], Batch: [600/600]Discriminator Loss: 1.3672, Generator Loss: 0.4158\n",
      "Epoch: [67/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7412\n",
      "Epoch: [67/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6324\n",
      "Epoch: [68/300], Batch: [300/600]Discriminator Loss: 1.3616, Generator Loss: 0.5570\n",
      "Epoch: [68/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7729\n",
      "Epoch: [68/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6483\n",
      "Epoch: [68/300], Batch: [600/600]Discriminator Loss: 1.4175, Generator Loss: 0.5930\n",
      "Epoch: [68/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7792\n",
      "Epoch: [68/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6691\n",
      "Epoch: [69/300], Batch: [300/600]Discriminator Loss: 1.3035, Generator Loss: 0.5648\n",
      "Epoch: [69/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7946\n",
      "Epoch: [69/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6396\n",
      "Epoch: [69/300], Batch: [600/600]Discriminator Loss: 1.3481, Generator Loss: 0.4720\n",
      "Epoch: [69/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7743\n",
      "Epoch: [69/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6509\n",
      "Epoch: [70/300], Batch: [300/600]Discriminator Loss: 1.2737, Generator Loss: 0.5209\n",
      "Epoch: [70/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7576\n",
      "Epoch: [70/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6148\n",
      "Epoch: [70/300], Batch: [600/600]Discriminator Loss: 1.3031, Generator Loss: 0.4677\n",
      "Epoch: [70/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8151\n",
      "Epoch: [70/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6435\n",
      "Generated images at epoch 70\n",
      "Epoch: [71/300], Batch: [300/600]Discriminator Loss: 1.3829, Generator Loss: 0.5415\n",
      "Epoch: [71/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8072\n",
      "Epoch: [71/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6536\n",
      "Epoch: [71/300], Batch: [600/600]Discriminator Loss: 1.3377, Generator Loss: 0.4977\n",
      "Epoch: [71/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7743\n",
      "Epoch: [71/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6377\n",
      "Epoch: [72/300], Batch: [300/600]Discriminator Loss: 1.2508, Generator Loss: 0.5523\n",
      "Epoch: [72/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7454\n",
      "Epoch: [72/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5963\n",
      "Epoch: [72/300], Batch: [600/600]Discriminator Loss: 1.2071, Generator Loss: 0.5324\n",
      "Epoch: [72/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7426\n",
      "Epoch: [72/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5660\n",
      "Epoch: [73/300], Batch: [300/600]Discriminator Loss: 1.4268, Generator Loss: 0.4731\n",
      "Epoch: [73/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7833\n",
      "Epoch: [73/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6685\n",
      "Epoch: [73/300], Batch: [600/600]Discriminator Loss: 1.2376, Generator Loss: 0.5359\n",
      "Epoch: [73/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7700\n",
      "Epoch: [73/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5975\n",
      "Epoch: [74/300], Batch: [300/600]Discriminator Loss: 1.2839, Generator Loss: 0.5423\n",
      "Epoch: [74/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7442\n",
      "Epoch: [74/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6104\n",
      "Epoch: [74/300], Batch: [600/600]Discriminator Loss: 1.3404, Generator Loss: 0.5188\n",
      "Epoch: [74/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8170\n",
      "Epoch: [74/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6511\n",
      "Epoch: [75/300], Batch: [300/600]Discriminator Loss: 1.2816, Generator Loss: 0.5409\n",
      "Epoch: [75/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7321\n",
      "Epoch: [75/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6019\n",
      "Epoch: [75/300], Batch: [600/600]Discriminator Loss: 1.3601, Generator Loss: 0.4125\n",
      "Epoch: [75/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7830\n",
      "Epoch: [75/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6399\n",
      "Epoch: [76/300], Batch: [300/600]Discriminator Loss: 1.2054, Generator Loss: 0.5405\n",
      "Epoch: [76/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7264\n",
      "Epoch: [76/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5658\n",
      "Epoch: [76/300], Batch: [600/600]Discriminator Loss: 1.2846, Generator Loss: 0.4396\n",
      "Epoch: [76/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7269\n",
      "Epoch: [76/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5965\n",
      "Epoch: [77/300], Batch: [300/600]Discriminator Loss: 1.3539, Generator Loss: 0.5797\n",
      "Epoch: [77/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7882\n",
      "Epoch: [77/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6512\n",
      "Epoch: [77/300], Batch: [600/600]Discriminator Loss: 1.3025, Generator Loss: 0.4343\n",
      "Epoch: [77/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7680\n",
      "Epoch: [77/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6293\n",
      "Epoch: [78/300], Batch: [300/600]Discriminator Loss: 1.3895, Generator Loss: 0.3711\n",
      "Epoch: [78/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7876\n",
      "Epoch: [78/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6599\n",
      "Epoch: [78/300], Batch: [600/600]Discriminator Loss: 1.2605, Generator Loss: 0.4928\n",
      "Epoch: [78/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7688\n",
      "Epoch: [78/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5923\n",
      "Epoch: [79/300], Batch: [300/600]Discriminator Loss: 1.3668, Generator Loss: 0.5493\n",
      "Epoch: [79/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8106\n",
      "Epoch: [79/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6605\n",
      "Epoch: [79/300], Batch: [600/600]Discriminator Loss: 1.2483, Generator Loss: 0.4902\n",
      "Epoch: [79/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7575\n",
      "Epoch: [79/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5997\n",
      "Epoch: [80/300], Batch: [300/600]Discriminator Loss: 1.2142, Generator Loss: 0.4650\n",
      "Epoch: [80/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7598\n",
      "Epoch: [80/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5860\n",
      "Epoch: [80/300], Batch: [600/600]Discriminator Loss: 1.3839, Generator Loss: 0.5702\n",
      "Epoch: [80/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7978\n",
      "Epoch: [80/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6665\n",
      "Generated images at epoch 80\n",
      "Epoch: [81/300], Batch: [300/600]Discriminator Loss: 1.4280, Generator Loss: 0.4834\n",
      "Epoch: [81/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8456\n",
      "Epoch: [81/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6915\n",
      "Epoch: [81/300], Batch: [600/600]Discriminator Loss: 1.3170, Generator Loss: 0.5316\n",
      "Epoch: [81/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7870\n",
      "Epoch: [81/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6388\n",
      "Epoch: [82/300], Batch: [300/600]Discriminator Loss: 1.2801, Generator Loss: 0.4524\n",
      "Epoch: [82/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7501\n",
      "Epoch: [82/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6035\n",
      "Epoch: [82/300], Batch: [600/600]Discriminator Loss: 1.3088, Generator Loss: 0.4703\n",
      "Epoch: [82/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8025\n",
      "Epoch: [82/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6376\n",
      "Epoch: [83/300], Batch: [300/600]Discriminator Loss: 1.2632, Generator Loss: 0.4141\n",
      "Epoch: [83/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7435\n",
      "Epoch: [83/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6001\n",
      "Epoch: [83/300], Batch: [600/600]Discriminator Loss: 1.4017, Generator Loss: 0.4118\n",
      "Epoch: [83/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8300\n",
      "Epoch: [83/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6772\n",
      "Epoch: [84/300], Batch: [300/600]Discriminator Loss: 1.1812, Generator Loss: 0.5727\n",
      "Epoch: [84/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7322\n",
      "Epoch: [84/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5545\n",
      "Epoch: [84/300], Batch: [600/600]Discriminator Loss: 1.3792, Generator Loss: 0.4262\n",
      "Epoch: [84/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8039\n",
      "Epoch: [84/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6643\n",
      "Epoch: [85/300], Batch: [300/600]Discriminator Loss: 1.4036, Generator Loss: 0.6181\n",
      "Epoch: [85/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8528\n",
      "Epoch: [85/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6861\n",
      "Epoch: [85/300], Batch: [600/600]Discriminator Loss: 1.2577, Generator Loss: 0.6292\n",
      "Epoch: [85/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7578\n",
      "Epoch: [85/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5950\n",
      "Epoch: [86/300], Batch: [300/600]Discriminator Loss: 1.2446, Generator Loss: 0.4718\n",
      "Epoch: [86/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7527\n",
      "Epoch: [86/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5938\n",
      "Epoch: [86/300], Batch: [600/600]Discriminator Loss: 1.3398, Generator Loss: 0.5717\n",
      "Epoch: [86/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7718\n",
      "Epoch: [86/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6423\n",
      "Epoch: [87/300], Batch: [300/600]Discriminator Loss: 1.1891, Generator Loss: 0.5665\n",
      "Epoch: [87/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7406\n",
      "Epoch: [87/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5633\n",
      "Epoch: [87/300], Batch: [600/600]Discriminator Loss: 1.3925, Generator Loss: 0.4816\n",
      "Epoch: [87/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7979\n",
      "Epoch: [87/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6638\n",
      "Epoch: [88/300], Batch: [300/600]Discriminator Loss: 1.2232, Generator Loss: 0.4682\n",
      "Epoch: [88/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7150\n",
      "Epoch: [88/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5685\n",
      "Epoch: [88/300], Batch: [600/600]Discriminator Loss: 1.1877, Generator Loss: 0.5741\n",
      "Epoch: [88/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7949\n",
      "Epoch: [88/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5919\n",
      "Epoch: [89/300], Batch: [300/600]Discriminator Loss: 1.2638, Generator Loss: 0.5511\n",
      "Epoch: [89/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7879\n",
      "Epoch: [89/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6139\n",
      "Epoch: [89/300], Batch: [600/600]Discriminator Loss: 1.2040, Generator Loss: 0.5999\n",
      "Epoch: [89/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7810\n",
      "Epoch: [89/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5949\n",
      "Epoch: [90/300], Batch: [300/600]Discriminator Loss: 1.4848, Generator Loss: 0.6077\n",
      "Epoch: [90/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7802\n",
      "Epoch: [90/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6791\n",
      "Epoch: [90/300], Batch: [600/600]Discriminator Loss: 1.4193, Generator Loss: 0.5649\n",
      "Epoch: [90/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8098\n",
      "Epoch: [90/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6813\n",
      "Generated images at epoch 90\n",
      "Epoch: [91/300], Batch: [300/600]Discriminator Loss: 1.2328, Generator Loss: 0.5457\n",
      "Epoch: [91/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7793\n",
      "Epoch: [91/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5854\n",
      "Epoch: [91/300], Batch: [600/600]Discriminator Loss: 1.2021, Generator Loss: 0.6130\n",
      "Epoch: [91/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7279\n",
      "Epoch: [91/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5618\n",
      "Epoch: [92/300], Batch: [300/600]Discriminator Loss: 1.1948, Generator Loss: 0.5460\n",
      "Epoch: [92/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7892\n",
      "Epoch: [92/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5875\n",
      "Epoch: [92/300], Batch: [600/600]Discriminator Loss: 1.2810, Generator Loss: 0.4108\n",
      "Epoch: [92/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7833\n",
      "Epoch: [92/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6144\n",
      "Epoch: [93/300], Batch: [300/600]Discriminator Loss: 1.2600, Generator Loss: 0.4103\n",
      "Epoch: [93/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8023\n",
      "Epoch: [93/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6211\n",
      "Epoch: [93/300], Batch: [600/600]Discriminator Loss: 1.1915, Generator Loss: 0.5086\n",
      "Epoch: [93/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7404\n",
      "Epoch: [93/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5720\n",
      "Epoch: [94/300], Batch: [300/600]Discriminator Loss: 1.2595, Generator Loss: 0.5744\n",
      "Epoch: [94/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8195\n",
      "Epoch: [94/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6276\n",
      "Epoch: [94/300], Batch: [600/600]Discriminator Loss: 1.2556, Generator Loss: 0.4837\n",
      "Epoch: [94/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7559\n",
      "Epoch: [94/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6066\n",
      "Epoch: [95/300], Batch: [300/600]Discriminator Loss: 1.1693, Generator Loss: 0.5580\n",
      "Epoch: [95/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7779\n",
      "Epoch: [95/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5646\n",
      "Epoch: [95/300], Batch: [600/600]Discriminator Loss: 1.3653, Generator Loss: 0.5121\n",
      "Epoch: [95/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8314\n",
      "Epoch: [95/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6739\n",
      "Epoch: [96/300], Batch: [300/600]Discriminator Loss: 1.2708, Generator Loss: 0.5071\n",
      "Epoch: [96/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7939\n",
      "Epoch: [96/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6170\n",
      "Epoch: [96/300], Batch: [600/600]Discriminator Loss: 1.3594, Generator Loss: 0.5743\n",
      "Epoch: [96/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7058\n",
      "Epoch: [96/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6160\n",
      "Epoch: [97/300], Batch: [300/600]Discriminator Loss: 1.4345, Generator Loss: 0.4336\n",
      "Epoch: [97/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8163\n",
      "Epoch: [97/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6781\n",
      "Epoch: [97/300], Batch: [600/600]Discriminator Loss: 1.2901, Generator Loss: 0.3725\n",
      "Epoch: [97/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7699\n",
      "Epoch: [97/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6218\n",
      "Epoch: [98/300], Batch: [300/600]Discriminator Loss: 1.3316, Generator Loss: 0.5697\n",
      "Epoch: [98/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8219\n",
      "Epoch: [98/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6592\n",
      "Epoch: [98/300], Batch: [600/600]Discriminator Loss: 1.3405, Generator Loss: 0.5437\n",
      "Epoch: [98/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7820\n",
      "Epoch: [98/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6428\n",
      "Epoch: [99/300], Batch: [300/600]Discriminator Loss: 1.1912, Generator Loss: 0.5630\n",
      "Epoch: [99/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7689\n",
      "Epoch: [99/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5730\n",
      "Epoch: [99/300], Batch: [600/600]Discriminator Loss: 1.2338, Generator Loss: 0.5617\n",
      "Epoch: [99/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7481\n",
      "Epoch: [99/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5891\n",
      "Epoch: [100/300], Batch: [300/600]Discriminator Loss: 1.2921, Generator Loss: 0.5376\n",
      "Epoch: [100/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8163\n",
      "Epoch: [100/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6287\n",
      "Epoch: [100/300], Batch: [600/600]Discriminator Loss: 1.4754, Generator Loss: 0.4149\n",
      "Epoch: [100/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7887\n",
      "Epoch: [100/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6905\n",
      "Generated images at epoch 100\n",
      "Epoch: [101/300], Batch: [300/600]Discriminator Loss: 1.1626, Generator Loss: 0.5039\n",
      "Epoch: [101/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7329\n",
      "Epoch: [101/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5497\n",
      "Epoch: [101/300], Batch: [600/600]Discriminator Loss: 1.3203, Generator Loss: 0.5718\n",
      "Epoch: [101/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7791\n",
      "Epoch: [101/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6217\n",
      "Epoch: [102/300], Batch: [300/600]Discriminator Loss: 1.3012, Generator Loss: 0.5566\n",
      "Epoch: [102/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8258\n",
      "Epoch: [102/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6449\n",
      "Epoch: [102/300], Batch: [600/600]Discriminator Loss: 1.4276, Generator Loss: 0.5104\n",
      "Epoch: [102/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8019\n",
      "Epoch: [102/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6700\n",
      "Epoch: [103/300], Batch: [300/600]Discriminator Loss: 1.1786, Generator Loss: 0.4841\n",
      "Epoch: [103/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7825\n",
      "Epoch: [103/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5814\n",
      "Epoch: [103/300], Batch: [600/600]Discriminator Loss: 1.2230, Generator Loss: 0.4862\n",
      "Epoch: [103/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7467\n",
      "Epoch: [103/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5779\n",
      "Epoch: [104/300], Batch: [300/600]Discriminator Loss: 1.2799, Generator Loss: 0.4912\n",
      "Epoch: [104/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7676\n",
      "Epoch: [104/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6201\n",
      "Epoch: [104/300], Batch: [600/600]Discriminator Loss: 1.2997, Generator Loss: 0.5338\n",
      "Epoch: [104/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7909\n",
      "Epoch: [104/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6273\n",
      "Epoch: [105/300], Batch: [300/600]Discriminator Loss: 1.4590, Generator Loss: 0.6119\n",
      "Epoch: [105/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8232\n",
      "Epoch: [105/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6963\n",
      "Epoch: [105/300], Batch: [600/600]Discriminator Loss: 1.2449, Generator Loss: 0.5536\n",
      "Epoch: [105/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7584\n",
      "Epoch: [105/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5957\n",
      "Epoch: [106/300], Batch: [300/600]Discriminator Loss: 1.2589, Generator Loss: 0.5267\n",
      "Epoch: [106/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7721\n",
      "Epoch: [106/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6047\n",
      "Epoch: [106/300], Batch: [600/600]Discriminator Loss: 1.3245, Generator Loss: 0.5425\n",
      "Epoch: [106/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7842\n",
      "Epoch: [106/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6439\n",
      "Epoch: [107/300], Batch: [300/600]Discriminator Loss: 1.2194, Generator Loss: 0.6249\n",
      "Epoch: [107/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7809\n",
      "Epoch: [107/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5955\n",
      "Epoch: [107/300], Batch: [600/600]Discriminator Loss: 1.2220, Generator Loss: 0.4727\n",
      "Epoch: [107/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7721\n",
      "Epoch: [107/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5974\n",
      "Epoch: [108/300], Batch: [300/600]Discriminator Loss: 1.3049, Generator Loss: 0.6008\n",
      "Epoch: [108/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7647\n",
      "Epoch: [108/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6230\n",
      "Epoch: [108/300], Batch: [600/600]Discriminator Loss: 1.2561, Generator Loss: 0.4937\n",
      "Epoch: [108/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7647\n",
      "Epoch: [108/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6004\n",
      "Epoch: [109/300], Batch: [300/600]Discriminator Loss: 1.2909, Generator Loss: 0.5649\n",
      "Epoch: [109/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7272\n",
      "Epoch: [109/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6033\n",
      "Epoch: [109/300], Batch: [600/600]Discriminator Loss: 1.4652, Generator Loss: 0.5837\n",
      "Epoch: [109/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8101\n",
      "Epoch: [109/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6889\n",
      "Epoch: [110/300], Batch: [300/600]Discriminator Loss: 1.2839, Generator Loss: 0.5279\n",
      "Epoch: [110/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8022\n",
      "Epoch: [110/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6240\n",
      "Epoch: [110/300], Batch: [600/600]Discriminator Loss: 1.2340, Generator Loss: 0.5721\n",
      "Epoch: [110/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7474\n",
      "Epoch: [110/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5915\n",
      "Generated images at epoch 110\n",
      "Epoch: [111/300], Batch: [300/600]Discriminator Loss: 1.2571, Generator Loss: 0.5118\n",
      "Epoch: [111/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7625\n",
      "Epoch: [111/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6024\n",
      "Epoch: [111/300], Batch: [600/600]Discriminator Loss: 1.3223, Generator Loss: 0.5669\n",
      "Epoch: [111/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8280\n",
      "Epoch: [111/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6510\n",
      "Epoch: [112/300], Batch: [300/600]Discriminator Loss: 1.2059, Generator Loss: 0.5048\n",
      "Epoch: [112/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7579\n",
      "Epoch: [112/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5845\n",
      "Epoch: [112/300], Batch: [600/600]Discriminator Loss: 1.2257, Generator Loss: 0.5344\n",
      "Epoch: [112/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7425\n",
      "Epoch: [112/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5849\n",
      "Epoch: [113/300], Batch: [300/600]Discriminator Loss: 1.1923, Generator Loss: 0.6214\n",
      "Epoch: [113/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7408\n",
      "Epoch: [113/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5647\n",
      "Epoch: [113/300], Batch: [600/600]Discriminator Loss: 1.3149, Generator Loss: 0.4817\n",
      "Epoch: [113/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7325\n",
      "Epoch: [113/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6069\n",
      "Epoch: [114/300], Batch: [300/600]Discriminator Loss: 1.3107, Generator Loss: 0.5525\n",
      "Epoch: [114/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7897\n",
      "Epoch: [114/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6362\n",
      "Epoch: [114/300], Batch: [600/600]Discriminator Loss: 1.2905, Generator Loss: 0.5681\n",
      "Epoch: [114/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7927\n",
      "Epoch: [114/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6256\n",
      "Epoch: [115/300], Batch: [300/600]Discriminator Loss: 1.2378, Generator Loss: 0.6572\n",
      "Epoch: [115/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8225\n",
      "Epoch: [115/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6156\n",
      "Epoch: [115/300], Batch: [600/600]Discriminator Loss: 1.3474, Generator Loss: 0.4388\n",
      "Epoch: [115/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7319\n",
      "Epoch: [115/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6229\n",
      "Epoch: [116/300], Batch: [300/600]Discriminator Loss: 1.2404, Generator Loss: 0.4709\n",
      "Epoch: [116/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8301\n",
      "Epoch: [116/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6251\n",
      "Epoch: [116/300], Batch: [600/600]Discriminator Loss: 1.1761, Generator Loss: 0.5075\n",
      "Epoch: [116/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7926\n",
      "Epoch: [116/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5865\n",
      "Epoch: [117/300], Batch: [300/600]Discriminator Loss: 1.2830, Generator Loss: 0.4089\n",
      "Epoch: [117/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7649\n",
      "Epoch: [117/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6214\n",
      "Epoch: [117/300], Batch: [600/600]Discriminator Loss: 1.2475, Generator Loss: 0.4115\n",
      "Epoch: [117/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7897\n",
      "Epoch: [117/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6193\n",
      "Epoch: [118/300], Batch: [300/600]Discriminator Loss: 1.2308, Generator Loss: 0.5271\n",
      "Epoch: [118/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8063\n",
      "Epoch: [118/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6143\n",
      "Epoch: [118/300], Batch: [600/600]Discriminator Loss: 1.4062, Generator Loss: 0.6681\n",
      "Epoch: [118/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8514\n",
      "Epoch: [118/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6920\n",
      "Epoch: [119/300], Batch: [300/600]Discriminator Loss: 1.1006, Generator Loss: 0.5618\n",
      "Epoch: [119/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7813\n",
      "Epoch: [119/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5525\n",
      "Epoch: [119/300], Batch: [600/600]Discriminator Loss: 1.2430, Generator Loss: 0.4894\n",
      "Epoch: [119/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7696\n",
      "Epoch: [119/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6035\n",
      "Epoch: [120/300], Batch: [300/600]Discriminator Loss: 1.1452, Generator Loss: 0.6297\n",
      "Epoch: [120/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8032\n",
      "Epoch: [120/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5733\n",
      "Epoch: [120/300], Batch: [600/600]Discriminator Loss: 1.2215, Generator Loss: 0.4000\n",
      "Epoch: [120/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7780\n",
      "Epoch: [120/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5922\n",
      "Generated images at epoch 120\n",
      "Epoch: [121/300], Batch: [300/600]Discriminator Loss: 1.3207, Generator Loss: 0.5284\n",
      "Epoch: [121/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8235\n",
      "Epoch: [121/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6546\n",
      "Epoch: [121/300], Batch: [600/600]Discriminator Loss: 1.3323, Generator Loss: 0.5384\n",
      "Epoch: [121/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8253\n",
      "Epoch: [121/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6556\n",
      "Epoch: [122/300], Batch: [300/600]Discriminator Loss: 1.3661, Generator Loss: 0.4082\n",
      "Epoch: [122/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8171\n",
      "Epoch: [122/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6673\n",
      "Epoch: [122/300], Batch: [600/600]Discriminator Loss: 1.3465, Generator Loss: 0.5459\n",
      "Epoch: [122/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7553\n",
      "Epoch: [122/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6251\n",
      "Epoch: [123/300], Batch: [300/600]Discriminator Loss: 1.3637, Generator Loss: 0.5421\n",
      "Epoch: [123/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7957\n",
      "Epoch: [123/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6459\n",
      "Epoch: [123/300], Batch: [600/600]Discriminator Loss: 1.2085, Generator Loss: 0.6025\n",
      "Epoch: [123/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7857\n",
      "Epoch: [123/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5892\n",
      "Epoch: [124/300], Batch: [300/600]Discriminator Loss: 1.2682, Generator Loss: 0.6210\n",
      "Epoch: [124/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8424\n",
      "Epoch: [124/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6361\n",
      "Epoch: [124/300], Batch: [600/600]Discriminator Loss: 1.2452, Generator Loss: 0.3973\n",
      "Epoch: [124/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7347\n",
      "Epoch: [124/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5762\n",
      "Epoch: [125/300], Batch: [300/600]Discriminator Loss: 1.2394, Generator Loss: 0.4674\n",
      "Epoch: [125/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7746\n",
      "Epoch: [125/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5997\n",
      "Epoch: [125/300], Batch: [600/600]Discriminator Loss: 1.2765, Generator Loss: 0.4443\n",
      "Epoch: [125/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7724\n",
      "Epoch: [125/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6163\n",
      "Epoch: [126/300], Batch: [300/600]Discriminator Loss: 1.1630, Generator Loss: 0.4441\n",
      "Epoch: [126/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8031\n",
      "Epoch: [126/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5926\n",
      "Epoch: [126/300], Batch: [600/600]Discriminator Loss: 1.2497, Generator Loss: 0.5061\n",
      "Epoch: [126/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7595\n",
      "Epoch: [126/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5975\n",
      "Epoch: [127/300], Batch: [300/600]Discriminator Loss: 1.1169, Generator Loss: 0.4494\n",
      "Epoch: [127/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7575\n",
      "Epoch: [127/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5303\n",
      "Epoch: [127/300], Batch: [600/600]Discriminator Loss: 1.2984, Generator Loss: 0.6028\n",
      "Epoch: [127/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8030\n",
      "Epoch: [127/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6395\n",
      "Epoch: [128/300], Batch: [300/600]Discriminator Loss: 1.3102, Generator Loss: 0.4782\n",
      "Epoch: [128/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7679\n",
      "Epoch: [128/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6290\n",
      "Epoch: [128/300], Batch: [600/600]Discriminator Loss: 1.2594, Generator Loss: 0.5727\n",
      "Epoch: [128/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8154\n",
      "Epoch: [128/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6084\n",
      "Epoch: [129/300], Batch: [300/600]Discriminator Loss: 1.2700, Generator Loss: 0.5365\n",
      "Epoch: [129/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8092\n",
      "Epoch: [129/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6161\n",
      "Epoch: [129/300], Batch: [600/600]Discriminator Loss: 1.1937, Generator Loss: 0.5978\n",
      "Epoch: [129/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7765\n",
      "Epoch: [129/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5802\n",
      "Epoch: [130/300], Batch: [300/600]Discriminator Loss: 1.3683, Generator Loss: 0.6280\n",
      "Epoch: [130/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8232\n",
      "Epoch: [130/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6556\n",
      "Epoch: [130/300], Batch: [600/600]Discriminator Loss: 1.3707, Generator Loss: 0.5180\n",
      "Epoch: [130/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8304\n",
      "Epoch: [130/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6745\n",
      "Generated images at epoch 130\n",
      "Epoch: [131/300], Batch: [300/600]Discriminator Loss: 1.1965, Generator Loss: 0.5288\n",
      "Epoch: [131/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7596\n",
      "Epoch: [131/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5782\n",
      "Epoch: [131/300], Batch: [600/600]Discriminator Loss: 1.2422, Generator Loss: 0.5641\n",
      "Epoch: [131/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8485\n",
      "Epoch: [131/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6342\n",
      "Epoch: [132/300], Batch: [300/600]Discriminator Loss: 1.3043, Generator Loss: 0.8192\n",
      "Epoch: [132/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8423\n",
      "Epoch: [132/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6320\n",
      "Epoch: [132/300], Batch: [600/600]Discriminator Loss: 1.3141, Generator Loss: 0.4840\n",
      "Epoch: [132/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7707\n",
      "Epoch: [132/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6266\n",
      "Epoch: [133/300], Batch: [300/600]Discriminator Loss: 1.2576, Generator Loss: 0.5844\n",
      "Epoch: [133/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7972\n",
      "Epoch: [133/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6143\n",
      "Epoch: [133/300], Batch: [600/600]Discriminator Loss: 1.2034, Generator Loss: 0.5373\n",
      "Epoch: [133/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7754\n",
      "Epoch: [133/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5882\n",
      "Epoch: [134/300], Batch: [300/600]Discriminator Loss: 1.0985, Generator Loss: 0.5145\n",
      "Epoch: [134/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7721\n",
      "Epoch: [134/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5377\n",
      "Epoch: [134/300], Batch: [600/600]Discriminator Loss: 1.3054, Generator Loss: 0.6146\n",
      "Epoch: [134/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7628\n",
      "Epoch: [134/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6114\n",
      "Epoch: [135/300], Batch: [300/600]Discriminator Loss: 1.3254, Generator Loss: 0.5975\n",
      "Epoch: [135/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7837\n",
      "Epoch: [135/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6317\n",
      "Epoch: [135/300], Batch: [600/600]Discriminator Loss: 1.3119, Generator Loss: 0.5634\n",
      "Epoch: [135/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7812\n",
      "Epoch: [135/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6291\n",
      "Epoch: [136/300], Batch: [300/600]Discriminator Loss: 1.3964, Generator Loss: 0.5049\n",
      "Epoch: [136/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8135\n",
      "Epoch: [136/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6709\n",
      "Epoch: [136/300], Batch: [600/600]Discriminator Loss: 1.2783, Generator Loss: 0.4479\n",
      "Epoch: [136/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7884\n",
      "Epoch: [136/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6213\n",
      "Epoch: [137/300], Batch: [300/600]Discriminator Loss: 1.2709, Generator Loss: 0.5764\n",
      "Epoch: [137/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8250\n",
      "Epoch: [137/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6322\n",
      "Epoch: [137/300], Batch: [600/600]Discriminator Loss: 1.2353, Generator Loss: 0.3875\n",
      "Epoch: [137/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8042\n",
      "Epoch: [137/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6159\n",
      "Epoch: [138/300], Batch: [300/600]Discriminator Loss: 1.2785, Generator Loss: 0.5991\n",
      "Epoch: [138/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8562\n",
      "Epoch: [138/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6522\n",
      "Epoch: [138/300], Batch: [600/600]Discriminator Loss: 1.2499, Generator Loss: 0.4280\n",
      "Epoch: [138/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8257\n",
      "Epoch: [138/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6295\n",
      "Epoch: [139/300], Batch: [300/600]Discriminator Loss: 1.1720, Generator Loss: 0.5598\n",
      "Epoch: [139/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7810\n",
      "Epoch: [139/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5715\n",
      "Epoch: [139/300], Batch: [600/600]Discriminator Loss: 1.2230, Generator Loss: 0.5084\n",
      "Epoch: [139/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7630\n",
      "Epoch: [139/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5820\n",
      "Epoch: [140/300], Batch: [300/600]Discriminator Loss: 1.2984, Generator Loss: 0.6683\n",
      "Epoch: [140/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7809\n",
      "Epoch: [140/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6260\n",
      "Epoch: [140/300], Batch: [600/600]Discriminator Loss: 1.1767, Generator Loss: 0.5963\n",
      "Epoch: [140/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7829\n",
      "Epoch: [140/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5789\n",
      "Generated images at epoch 140\n",
      "Epoch: [141/300], Batch: [300/600]Discriminator Loss: 1.2102, Generator Loss: 0.7221\n",
      "Epoch: [141/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8116\n",
      "Epoch: [141/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5987\n",
      "Epoch: [141/300], Batch: [600/600]Discriminator Loss: 1.2319, Generator Loss: 0.5101\n",
      "Epoch: [141/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7869\n",
      "Epoch: [141/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5991\n",
      "Epoch: [142/300], Batch: [300/600]Discriminator Loss: 1.2278, Generator Loss: 0.6095\n",
      "Epoch: [142/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7812\n",
      "Epoch: [142/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6062\n",
      "Epoch: [142/300], Batch: [600/600]Discriminator Loss: 1.3803, Generator Loss: 0.4927\n",
      "Epoch: [142/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8371\n",
      "Epoch: [142/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6762\n",
      "Epoch: [143/300], Batch: [300/600]Discriminator Loss: 1.2777, Generator Loss: 0.6080\n",
      "Epoch: [143/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7861\n",
      "Epoch: [143/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6282\n",
      "Epoch: [143/300], Batch: [600/600]Discriminator Loss: 1.3147, Generator Loss: 0.5744\n",
      "Epoch: [143/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7939\n",
      "Epoch: [143/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6354\n",
      "Epoch: [144/300], Batch: [300/600]Discriminator Loss: 1.2571, Generator Loss: 0.5124\n",
      "Epoch: [144/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7143\n",
      "Epoch: [144/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5807\n",
      "Epoch: [144/300], Batch: [600/600]Discriminator Loss: 1.2058, Generator Loss: 0.5232\n",
      "Epoch: [144/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7686\n",
      "Epoch: [144/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5924\n",
      "Epoch: [145/300], Batch: [300/600]Discriminator Loss: 1.2038, Generator Loss: 0.4471\n",
      "Epoch: [145/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7335\n",
      "Epoch: [145/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5645\n",
      "Epoch: [145/300], Batch: [600/600]Discriminator Loss: 1.4695, Generator Loss: 0.4422\n",
      "Epoch: [145/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7827\n",
      "Epoch: [145/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6797\n",
      "Epoch: [146/300], Batch: [300/600]Discriminator Loss: 1.1882, Generator Loss: 0.4702\n",
      "Epoch: [146/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7687\n",
      "Epoch: [146/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5666\n",
      "Epoch: [146/300], Batch: [600/600]Discriminator Loss: 1.3685, Generator Loss: 0.5197\n",
      "Epoch: [146/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8015\n",
      "Epoch: [146/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6625\n",
      "Epoch: [147/300], Batch: [300/600]Discriminator Loss: 1.3219, Generator Loss: 0.5693\n",
      "Epoch: [147/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7858\n",
      "Epoch: [147/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6357\n",
      "Epoch: [147/300], Batch: [600/600]Discriminator Loss: 1.1531, Generator Loss: 0.4968\n",
      "Epoch: [147/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7392\n",
      "Epoch: [147/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5516\n",
      "Epoch: [148/300], Batch: [300/600]Discriminator Loss: 1.1312, Generator Loss: 0.4879\n",
      "Epoch: [148/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8332\n",
      "Epoch: [148/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5872\n",
      "Epoch: [148/300], Batch: [600/600]Discriminator Loss: 1.2127, Generator Loss: 0.5325\n",
      "Epoch: [148/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7688\n",
      "Epoch: [148/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5826\n",
      "Epoch: [149/300], Batch: [300/600]Discriminator Loss: 1.2068, Generator Loss: 0.4324\n",
      "Epoch: [149/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7161\n",
      "Epoch: [149/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5611\n",
      "Epoch: [149/300], Batch: [600/600]Discriminator Loss: 1.2707, Generator Loss: 0.5296\n",
      "Epoch: [149/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8094\n",
      "Epoch: [149/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6220\n",
      "Epoch: [150/300], Batch: [300/600]Discriminator Loss: 1.2364, Generator Loss: 0.6276\n",
      "Epoch: [150/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8068\n",
      "Epoch: [150/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6075\n",
      "Epoch: [150/300], Batch: [600/600]Discriminator Loss: 1.2862, Generator Loss: 0.5746\n",
      "Epoch: [150/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8085\n",
      "Epoch: [150/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6340\n",
      "Generated images at epoch 150\n",
      "Epoch: [151/300], Batch: [300/600]Discriminator Loss: 1.1812, Generator Loss: 0.4752\n",
      "Epoch: [151/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7421\n",
      "Epoch: [151/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5699\n",
      "Epoch: [151/300], Batch: [600/600]Discriminator Loss: 1.2674, Generator Loss: 0.4895\n",
      "Epoch: [151/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8410\n",
      "Epoch: [151/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6378\n",
      "Epoch: [152/300], Batch: [300/600]Discriminator Loss: 1.2216, Generator Loss: 0.5311\n",
      "Epoch: [152/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8108\n",
      "Epoch: [152/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6092\n",
      "Epoch: [152/300], Batch: [600/600]Discriminator Loss: 1.3282, Generator Loss: 0.6273\n",
      "Epoch: [152/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8068\n",
      "Epoch: [152/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6553\n",
      "Epoch: [153/300], Batch: [300/600]Discriminator Loss: 1.3490, Generator Loss: 0.5705\n",
      "Epoch: [153/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7609\n",
      "Epoch: [153/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6319\n",
      "Epoch: [153/300], Batch: [600/600]Discriminator Loss: 1.2018, Generator Loss: 0.4232\n",
      "Epoch: [153/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7166\n",
      "Epoch: [153/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5329\n",
      "Epoch: [154/300], Batch: [300/600]Discriminator Loss: 1.2416, Generator Loss: 0.5258\n",
      "Epoch: [154/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7969\n",
      "Epoch: [154/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6140\n",
      "Epoch: [154/300], Batch: [600/600]Discriminator Loss: 1.2317, Generator Loss: 0.6050\n",
      "Epoch: [154/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8160\n",
      "Epoch: [154/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6137\n",
      "Epoch: [155/300], Batch: [300/600]Discriminator Loss: 1.2781, Generator Loss: 0.5383\n",
      "Epoch: [155/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8153\n",
      "Epoch: [155/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6402\n",
      "Epoch: [155/300], Batch: [600/600]Discriminator Loss: 1.1694, Generator Loss: 0.4433\n",
      "Epoch: [155/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7760\n",
      "Epoch: [155/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5676\n",
      "Epoch: [156/300], Batch: [300/600]Discriminator Loss: 1.2308, Generator Loss: 0.5014\n",
      "Epoch: [156/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8395\n",
      "Epoch: [156/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6264\n",
      "Epoch: [156/300], Batch: [600/600]Discriminator Loss: 1.1460, Generator Loss: 0.5979\n",
      "Epoch: [156/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7832\n",
      "Epoch: [156/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5680\n",
      "Epoch: [157/300], Batch: [300/600]Discriminator Loss: 1.1097, Generator Loss: 0.5033\n",
      "Epoch: [157/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8367\n",
      "Epoch: [157/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5715\n",
      "Epoch: [157/300], Batch: [600/600]Discriminator Loss: 1.2785, Generator Loss: 0.5122\n",
      "Epoch: [157/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7793\n",
      "Epoch: [157/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6206\n",
      "Epoch: [158/300], Batch: [300/600]Discriminator Loss: 1.1998, Generator Loss: 0.5666\n",
      "Epoch: [158/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8040\n",
      "Epoch: [158/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6017\n",
      "Epoch: [158/300], Batch: [600/600]Discriminator Loss: 1.2575, Generator Loss: 0.4764\n",
      "Epoch: [158/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8252\n",
      "Epoch: [158/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6359\n",
      "Epoch: [159/300], Batch: [300/600]Discriminator Loss: 1.1666, Generator Loss: 0.4716\n",
      "Epoch: [159/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7328\n",
      "Epoch: [159/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5413\n",
      "Epoch: [159/300], Batch: [600/600]Discriminator Loss: 1.2519, Generator Loss: 0.5686\n",
      "Epoch: [159/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8519\n",
      "Epoch: [159/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6448\n",
      "Epoch: [160/300], Batch: [300/600]Discriminator Loss: 1.3148, Generator Loss: 0.6042\n",
      "Epoch: [160/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8071\n",
      "Epoch: [160/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6372\n",
      "Epoch: [160/300], Batch: [600/600]Discriminator Loss: 1.1676, Generator Loss: 0.5414\n",
      "Epoch: [160/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7741\n",
      "Epoch: [160/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5728\n",
      "Generated images at epoch 160\n",
      "Epoch: [161/300], Batch: [300/600]Discriminator Loss: 1.0698, Generator Loss: 0.5331\n",
      "Epoch: [161/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7901\n",
      "Epoch: [161/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5304\n",
      "Epoch: [161/300], Batch: [600/600]Discriminator Loss: 1.2256, Generator Loss: 0.4622\n",
      "Epoch: [161/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7892\n",
      "Epoch: [161/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6022\n",
      "Epoch: [162/300], Batch: [300/600]Discriminator Loss: 1.1591, Generator Loss: 0.4627\n",
      "Epoch: [162/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7967\n",
      "Epoch: [162/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5788\n",
      "Epoch: [162/300], Batch: [600/600]Discriminator Loss: 1.1590, Generator Loss: 0.5953\n",
      "Epoch: [162/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7148\n",
      "Epoch: [162/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5267\n",
      "Epoch: [163/300], Batch: [300/600]Discriminator Loss: 1.2013, Generator Loss: 0.4771\n",
      "Epoch: [163/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7301\n",
      "Epoch: [163/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5701\n",
      "Epoch: [163/300], Batch: [600/600]Discriminator Loss: 1.1718, Generator Loss: 0.5511\n",
      "Epoch: [163/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8460\n",
      "Epoch: [163/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5932\n",
      "Epoch: [164/300], Batch: [300/600]Discriminator Loss: 1.4236, Generator Loss: 0.5871\n",
      "Epoch: [164/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8373\n",
      "Epoch: [164/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6932\n",
      "Epoch: [164/300], Batch: [600/600]Discriminator Loss: 1.2121, Generator Loss: 0.6181\n",
      "Epoch: [164/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8052\n",
      "Epoch: [164/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6081\n",
      "Epoch: [165/300], Batch: [300/600]Discriminator Loss: 1.2735, Generator Loss: 0.5988\n",
      "Epoch: [165/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8589\n",
      "Epoch: [165/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6451\n",
      "Epoch: [165/300], Batch: [600/600]Discriminator Loss: 1.3163, Generator Loss: 0.5778\n",
      "Epoch: [165/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8158\n",
      "Epoch: [165/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6460\n",
      "Epoch: [166/300], Batch: [300/600]Discriminator Loss: 1.2630, Generator Loss: 0.4901\n",
      "Epoch: [166/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7396\n",
      "Epoch: [166/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5893\n",
      "Epoch: [166/300], Batch: [600/600]Discriminator Loss: 1.4264, Generator Loss: 0.4155\n",
      "Epoch: [166/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8594\n",
      "Epoch: [166/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6950\n",
      "Epoch: [167/300], Batch: [300/600]Discriminator Loss: 1.2273, Generator Loss: 0.4373\n",
      "Epoch: [167/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8183\n",
      "Epoch: [167/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6027\n",
      "Epoch: [167/300], Batch: [600/600]Discriminator Loss: 1.2668, Generator Loss: 0.5886\n",
      "Epoch: [167/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7750\n",
      "Epoch: [167/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6133\n",
      "Epoch: [168/300], Batch: [300/600]Discriminator Loss: 1.0863, Generator Loss: 0.6068\n",
      "Epoch: [168/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7401\n",
      "Epoch: [168/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5191\n",
      "Epoch: [168/300], Batch: [600/600]Discriminator Loss: 1.1786, Generator Loss: 0.4898\n",
      "Epoch: [168/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7796\n",
      "Epoch: [168/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5797\n",
      "Epoch: [169/300], Batch: [300/600]Discriminator Loss: 1.2644, Generator Loss: 0.5183\n",
      "Epoch: [169/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7601\n",
      "Epoch: [169/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6065\n",
      "Epoch: [169/300], Batch: [600/600]Discriminator Loss: 1.1180, Generator Loss: 0.5777\n",
      "Epoch: [169/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7536\n",
      "Epoch: [169/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5460\n",
      "Epoch: [170/300], Batch: [300/600]Discriminator Loss: 1.3051, Generator Loss: 0.4595\n",
      "Epoch: [170/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7803\n",
      "Epoch: [170/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6336\n",
      "Epoch: [170/300], Batch: [600/600]Discriminator Loss: 1.1154, Generator Loss: 0.6073\n",
      "Epoch: [170/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8090\n",
      "Epoch: [170/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5667\n",
      "Generated images at epoch 170\n",
      "Epoch: [171/300], Batch: [300/600]Discriminator Loss: 1.1383, Generator Loss: 0.5338\n",
      "Epoch: [171/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8002\n",
      "Epoch: [171/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5696\n",
      "Epoch: [171/300], Batch: [600/600]Discriminator Loss: 1.3753, Generator Loss: 0.6660\n",
      "Epoch: [171/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8514\n",
      "Epoch: [171/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6639\n",
      "Epoch: [172/300], Batch: [300/600]Discriminator Loss: 1.2243, Generator Loss: 0.3514\n",
      "Epoch: [172/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7847\n",
      "Epoch: [172/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6040\n",
      "Epoch: [172/300], Batch: [600/600]Discriminator Loss: 1.2691, Generator Loss: 0.4434\n",
      "Epoch: [172/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7991\n",
      "Epoch: [172/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6161\n",
      "Epoch: [173/300], Batch: [300/600]Discriminator Loss: 1.1608, Generator Loss: 0.6223\n",
      "Epoch: [173/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8019\n",
      "Epoch: [173/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5857\n",
      "Epoch: [173/300], Batch: [600/600]Discriminator Loss: 1.2171, Generator Loss: 0.4842\n",
      "Epoch: [173/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8119\n",
      "Epoch: [173/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6133\n",
      "Epoch: [174/300], Batch: [300/600]Discriminator Loss: 1.0857, Generator Loss: 0.7102\n",
      "Epoch: [174/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7911\n",
      "Epoch: [174/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5395\n",
      "Epoch: [174/300], Batch: [600/600]Discriminator Loss: 1.1135, Generator Loss: 0.6273\n",
      "Epoch: [174/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7902\n",
      "Epoch: [174/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5480\n",
      "Epoch: [175/300], Batch: [300/600]Discriminator Loss: 1.1757, Generator Loss: 0.5462\n",
      "Epoch: [175/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7716\n",
      "Epoch: [175/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5776\n",
      "Epoch: [175/300], Batch: [600/600]Discriminator Loss: 1.1608, Generator Loss: 0.6821\n",
      "Epoch: [175/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7858\n",
      "Epoch: [175/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5698\n",
      "Epoch: [176/300], Batch: [300/600]Discriminator Loss: 1.1364, Generator Loss: 0.4983\n",
      "Epoch: [176/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8127\n",
      "Epoch: [176/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5672\n",
      "Epoch: [176/300], Batch: [600/600]Discriminator Loss: 1.3041, Generator Loss: 0.4865\n",
      "Epoch: [176/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8101\n",
      "Epoch: [176/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6470\n",
      "Epoch: [177/300], Batch: [300/600]Discriminator Loss: 1.1416, Generator Loss: 0.5793\n",
      "Epoch: [177/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7819\n",
      "Epoch: [177/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5705\n",
      "Epoch: [177/300], Batch: [600/600]Discriminator Loss: 1.2901, Generator Loss: 0.5262\n",
      "Epoch: [177/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7688\n",
      "Epoch: [177/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6215\n",
      "Epoch: [178/300], Batch: [300/600]Discriminator Loss: 1.2234, Generator Loss: 0.5210\n",
      "Epoch: [178/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7599\n",
      "Epoch: [178/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5785\n",
      "Epoch: [178/300], Batch: [600/600]Discriminator Loss: 1.3435, Generator Loss: 0.4671\n",
      "Epoch: [178/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7457\n",
      "Epoch: [178/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6251\n",
      "Epoch: [179/300], Batch: [300/600]Discriminator Loss: 1.2544, Generator Loss: 0.4581\n",
      "Epoch: [179/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8020\n",
      "Epoch: [179/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6255\n",
      "Epoch: [179/300], Batch: [600/600]Discriminator Loss: 1.0282, Generator Loss: 0.5546\n",
      "Epoch: [179/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7762\n",
      "Epoch: [179/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5041\n",
      "Epoch: [180/300], Batch: [300/600]Discriminator Loss: 1.2414, Generator Loss: 0.4699\n",
      "Epoch: [180/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8082\n",
      "Epoch: [180/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6090\n",
      "Epoch: [180/300], Batch: [600/600]Discriminator Loss: 1.2974, Generator Loss: 0.5008\n",
      "Epoch: [180/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8162\n",
      "Epoch: [180/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6388\n",
      "Generated images at epoch 180\n",
      "Epoch: [181/300], Batch: [300/600]Discriminator Loss: 1.1485, Generator Loss: 0.5591\n",
      "Epoch: [181/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.6713\n",
      "Epoch: [181/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5064\n",
      "Epoch: [181/300], Batch: [600/600]Discriminator Loss: 1.3713, Generator Loss: 0.4963\n",
      "Epoch: [181/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8515\n",
      "Epoch: [181/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6710\n",
      "Epoch: [182/300], Batch: [300/600]Discriminator Loss: 1.2903, Generator Loss: 0.4908\n",
      "Epoch: [182/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7911\n",
      "Epoch: [182/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6291\n",
      "Epoch: [182/300], Batch: [600/600]Discriminator Loss: 1.3491, Generator Loss: 0.5855\n",
      "Epoch: [182/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8182\n",
      "Epoch: [182/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6542\n",
      "Epoch: [183/300], Batch: [300/600]Discriminator Loss: 1.4259, Generator Loss: 0.5397\n",
      "Epoch: [183/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8217\n",
      "Epoch: [183/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6855\n",
      "Epoch: [183/300], Batch: [600/600]Discriminator Loss: 1.3922, Generator Loss: 0.5668\n",
      "Epoch: [183/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8024\n",
      "Epoch: [183/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6665\n",
      "Epoch: [184/300], Batch: [300/600]Discriminator Loss: 1.1687, Generator Loss: 0.5842\n",
      "Epoch: [184/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7390\n",
      "Epoch: [184/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5469\n",
      "Epoch: [184/300], Batch: [600/600]Discriminator Loss: 1.1706, Generator Loss: 0.5493\n",
      "Epoch: [184/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7974\n",
      "Epoch: [184/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5844\n",
      "Epoch: [185/300], Batch: [300/600]Discriminator Loss: 1.1823, Generator Loss: 0.5838\n",
      "Epoch: [185/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7773\n",
      "Epoch: [185/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5741\n",
      "Epoch: [185/300], Batch: [600/600]Discriminator Loss: 1.2774, Generator Loss: 0.4385\n",
      "Epoch: [185/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7636\n",
      "Epoch: [185/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6165\n",
      "Epoch: [186/300], Batch: [300/600]Discriminator Loss: 1.2490, Generator Loss: 0.4555\n",
      "Epoch: [186/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7894\n",
      "Epoch: [186/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6019\n",
      "Epoch: [186/300], Batch: [600/600]Discriminator Loss: 1.1685, Generator Loss: 0.7888\n",
      "Epoch: [186/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7932\n",
      "Epoch: [186/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5786\n",
      "Epoch: [187/300], Batch: [300/600]Discriminator Loss: 1.0864, Generator Loss: 0.5178\n",
      "Epoch: [187/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8131\n",
      "Epoch: [187/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5588\n",
      "Epoch: [187/300], Batch: [600/600]Discriminator Loss: 1.3407, Generator Loss: 0.5733\n",
      "Epoch: [187/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8283\n",
      "Epoch: [187/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6595\n",
      "Epoch: [188/300], Batch: [300/600]Discriminator Loss: 1.1585, Generator Loss: 0.6262\n",
      "Epoch: [188/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7512\n",
      "Epoch: [188/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5558\n",
      "Epoch: [188/300], Batch: [600/600]Discriminator Loss: 1.2830, Generator Loss: 0.5491\n",
      "Epoch: [188/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8060\n",
      "Epoch: [188/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6250\n",
      "Epoch: [189/300], Batch: [300/600]Discriminator Loss: 1.3704, Generator Loss: 0.4481\n",
      "Epoch: [189/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8579\n",
      "Epoch: [189/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6789\n",
      "Epoch: [189/300], Batch: [600/600]Discriminator Loss: 1.1852, Generator Loss: 0.4334\n",
      "Epoch: [189/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7898\n",
      "Epoch: [189/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5933\n",
      "Epoch: [190/300], Batch: [300/600]Discriminator Loss: 1.5029, Generator Loss: 0.5212\n",
      "Epoch: [190/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8604\n",
      "Epoch: [190/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.7208\n",
      "Epoch: [190/300], Batch: [600/600]Discriminator Loss: 1.0641, Generator Loss: 0.5332\n",
      "Epoch: [190/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7324\n",
      "Epoch: [190/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.4964\n",
      "Generated images at epoch 190\n",
      "Epoch: [191/300], Batch: [300/600]Discriminator Loss: 1.4155, Generator Loss: 0.4506\n",
      "Epoch: [191/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8368\n",
      "Epoch: [191/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6900\n",
      "Epoch: [191/300], Batch: [600/600]Discriminator Loss: 1.0999, Generator Loss: 0.5664\n",
      "Epoch: [191/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7100\n",
      "Epoch: [191/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.4947\n",
      "Epoch: [192/300], Batch: [300/600]Discriminator Loss: 1.2418, Generator Loss: 0.4619\n",
      "Epoch: [192/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7863\n",
      "Epoch: [192/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6176\n",
      "Epoch: [192/300], Batch: [600/600]Discriminator Loss: 1.2457, Generator Loss: 0.4495\n",
      "Epoch: [192/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7646\n",
      "Epoch: [192/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5883\n",
      "Epoch: [193/300], Batch: [300/600]Discriminator Loss: 1.2035, Generator Loss: 0.5121\n",
      "Epoch: [193/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7940\n",
      "Epoch: [193/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5918\n",
      "Epoch: [193/300], Batch: [600/600]Discriminator Loss: 1.2666, Generator Loss: 0.4830\n",
      "Epoch: [193/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8349\n",
      "Epoch: [193/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6465\n",
      "Epoch: [194/300], Batch: [300/600]Discriminator Loss: 1.1264, Generator Loss: 0.5692\n",
      "Epoch: [194/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8484\n",
      "Epoch: [194/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5747\n",
      "Epoch: [194/300], Batch: [600/600]Discriminator Loss: 1.2925, Generator Loss: 0.3808\n",
      "Epoch: [194/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7649\n",
      "Epoch: [194/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6120\n",
      "Epoch: [195/300], Batch: [300/600]Discriminator Loss: 1.3754, Generator Loss: 0.4401\n",
      "Epoch: [195/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7889\n",
      "Epoch: [195/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6674\n",
      "Epoch: [195/300], Batch: [600/600]Discriminator Loss: 1.3362, Generator Loss: 0.6828\n",
      "Epoch: [195/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8425\n",
      "Epoch: [195/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6587\n",
      "Epoch: [196/300], Batch: [300/600]Discriminator Loss: 1.2023, Generator Loss: 0.6149\n",
      "Epoch: [196/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7474\n",
      "Epoch: [196/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5773\n",
      "Epoch: [196/300], Batch: [600/600]Discriminator Loss: 1.2445, Generator Loss: 0.6001\n",
      "Epoch: [196/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8301\n",
      "Epoch: [196/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6233\n",
      "Epoch: [197/300], Batch: [300/600]Discriminator Loss: 1.2250, Generator Loss: 0.5251\n",
      "Epoch: [197/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7551\n",
      "Epoch: [197/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5644\n",
      "Epoch: [197/300], Batch: [600/600]Discriminator Loss: 1.1949, Generator Loss: 0.3690\n",
      "Epoch: [197/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8156\n",
      "Epoch: [197/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6081\n",
      "Epoch: [198/300], Batch: [300/600]Discriminator Loss: 1.2439, Generator Loss: 0.5979\n",
      "Epoch: [198/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7738\n",
      "Epoch: [198/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6021\n",
      "Epoch: [198/300], Batch: [600/600]Discriminator Loss: 1.1739, Generator Loss: 0.4335\n",
      "Epoch: [198/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7354\n",
      "Epoch: [198/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5425\n",
      "Epoch: [199/300], Batch: [300/600]Discriminator Loss: 1.2777, Generator Loss: 0.6473\n",
      "Epoch: [199/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7980\n",
      "Epoch: [199/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6280\n",
      "Epoch: [199/300], Batch: [600/600]Discriminator Loss: 1.3895, Generator Loss: 0.5858\n",
      "Epoch: [199/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7858\n",
      "Epoch: [199/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6552\n",
      "Epoch: [200/300], Batch: [300/600]Discriminator Loss: 1.4352, Generator Loss: 0.6906\n",
      "Epoch: [200/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8336\n",
      "Epoch: [200/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6921\n",
      "Epoch: [200/300], Batch: [600/600]Discriminator Loss: 1.2465, Generator Loss: 0.5612\n",
      "Epoch: [200/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7998\n",
      "Epoch: [200/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6225\n",
      "Generated images at epoch 200\n",
      "Epoch: [201/300], Batch: [300/600]Discriminator Loss: 1.2795, Generator Loss: 0.4473\n",
      "Epoch: [201/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8151\n",
      "Epoch: [201/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6364\n",
      "Epoch: [201/300], Batch: [600/600]Discriminator Loss: 1.2992, Generator Loss: 0.6184\n",
      "Epoch: [201/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8468\n",
      "Epoch: [201/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6368\n",
      "Epoch: [202/300], Batch: [300/600]Discriminator Loss: 1.2883, Generator Loss: 0.4015\n",
      "Epoch: [202/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7991\n",
      "Epoch: [202/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6385\n",
      "Epoch: [202/300], Batch: [600/600]Discriminator Loss: 1.2115, Generator Loss: 0.5035\n",
      "Epoch: [202/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7000\n",
      "Epoch: [202/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5524\n",
      "Epoch: [203/300], Batch: [300/600]Discriminator Loss: 1.3651, Generator Loss: 0.4420\n",
      "Epoch: [203/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7907\n",
      "Epoch: [203/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6580\n",
      "Epoch: [203/300], Batch: [600/600]Discriminator Loss: 1.2992, Generator Loss: 0.5084\n",
      "Epoch: [203/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7780\n",
      "Epoch: [203/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6320\n",
      "Epoch: [204/300], Batch: [300/600]Discriminator Loss: 1.2209, Generator Loss: 0.4626\n",
      "Epoch: [204/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7907\n",
      "Epoch: [204/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5962\n",
      "Epoch: [204/300], Batch: [600/600]Discriminator Loss: 1.3083, Generator Loss: 0.5915\n",
      "Epoch: [204/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8227\n",
      "Epoch: [204/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6391\n",
      "Epoch: [205/300], Batch: [300/600]Discriminator Loss: 1.2171, Generator Loss: 0.6127\n",
      "Epoch: [205/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8112\n",
      "Epoch: [205/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6119\n",
      "Epoch: [205/300], Batch: [600/600]Discriminator Loss: 1.2666, Generator Loss: 0.4189\n",
      "Epoch: [205/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7898\n",
      "Epoch: [205/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6138\n",
      "Epoch: [206/300], Batch: [300/600]Discriminator Loss: 1.2537, Generator Loss: 0.7059\n",
      "Epoch: [206/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8198\n",
      "Epoch: [206/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6081\n",
      "Epoch: [206/300], Batch: [600/600]Discriminator Loss: 1.3108, Generator Loss: 0.5615\n",
      "Epoch: [206/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8230\n",
      "Epoch: [206/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6479\n",
      "Epoch: [207/300], Batch: [300/600]Discriminator Loss: 1.2661, Generator Loss: 0.5603\n",
      "Epoch: [207/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7538\n",
      "Epoch: [207/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6049\n",
      "Epoch: [207/300], Batch: [600/600]Discriminator Loss: 1.1555, Generator Loss: 0.5238\n",
      "Epoch: [207/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7855\n",
      "Epoch: [207/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5634\n",
      "Epoch: [208/300], Batch: [300/600]Discriminator Loss: 1.1793, Generator Loss: 0.4695\n",
      "Epoch: [208/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7933\n",
      "Epoch: [208/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5936\n",
      "Epoch: [208/300], Batch: [600/600]Discriminator Loss: 1.2929, Generator Loss: 0.5096\n",
      "Epoch: [208/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7848\n",
      "Epoch: [208/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6253\n",
      "Epoch: [209/300], Batch: [300/600]Discriminator Loss: 1.3007, Generator Loss: 0.4416\n",
      "Epoch: [209/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8347\n",
      "Epoch: [209/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6451\n",
      "Epoch: [209/300], Batch: [600/600]Discriminator Loss: 1.3384, Generator Loss: 0.4272\n",
      "Epoch: [209/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7971\n",
      "Epoch: [209/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6459\n",
      "Epoch: [210/300], Batch: [300/600]Discriminator Loss: 1.2258, Generator Loss: 0.4224\n",
      "Epoch: [210/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8022\n",
      "Epoch: [210/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6141\n",
      "Epoch: [210/300], Batch: [600/600]Discriminator Loss: 1.1368, Generator Loss: 0.4981\n",
      "Epoch: [210/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7216\n",
      "Epoch: [210/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5056\n",
      "Generated images at epoch 210\n",
      "Epoch: [211/300], Batch: [300/600]Discriminator Loss: 1.0922, Generator Loss: 0.4963\n",
      "Epoch: [211/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7793\n",
      "Epoch: [211/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5328\n",
      "Epoch: [211/300], Batch: [600/600]Discriminator Loss: 1.1773, Generator Loss: 0.4237\n",
      "Epoch: [211/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7542\n",
      "Epoch: [211/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5744\n",
      "Epoch: [212/300], Batch: [300/600]Discriminator Loss: 1.1825, Generator Loss: 0.4743\n",
      "Epoch: [212/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7353\n",
      "Epoch: [212/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5512\n",
      "Epoch: [212/300], Batch: [600/600]Discriminator Loss: 1.3119, Generator Loss: 0.5333\n",
      "Epoch: [212/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8637\n",
      "Epoch: [212/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6621\n",
      "Epoch: [213/300], Batch: [300/600]Discriminator Loss: 1.2599, Generator Loss: 0.4000\n",
      "Epoch: [213/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8293\n",
      "Epoch: [213/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6165\n",
      "Epoch: [213/300], Batch: [600/600]Discriminator Loss: 1.0796, Generator Loss: 0.4356\n",
      "Epoch: [213/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7935\n",
      "Epoch: [213/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5526\n",
      "Epoch: [214/300], Batch: [300/600]Discriminator Loss: 1.2604, Generator Loss: 0.5545\n",
      "Epoch: [214/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7850\n",
      "Epoch: [214/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6158\n",
      "Epoch: [214/300], Batch: [600/600]Discriminator Loss: 1.1169, Generator Loss: 0.6274\n",
      "Epoch: [214/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8205\n",
      "Epoch: [214/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5687\n",
      "Epoch: [215/300], Batch: [300/600]Discriminator Loss: 1.4181, Generator Loss: 0.5149\n",
      "Epoch: [215/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8600\n",
      "Epoch: [215/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6965\n",
      "Epoch: [215/300], Batch: [600/600]Discriminator Loss: 1.1660, Generator Loss: 0.5561\n",
      "Epoch: [215/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8379\n",
      "Epoch: [215/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6049\n",
      "Epoch: [216/300], Batch: [300/600]Discriminator Loss: 1.1043, Generator Loss: 0.5856\n",
      "Epoch: [216/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7963\n",
      "Epoch: [216/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5520\n",
      "Epoch: [216/300], Batch: [600/600]Discriminator Loss: 1.2707, Generator Loss: 0.4888\n",
      "Epoch: [216/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7761\n",
      "Epoch: [216/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6103\n",
      "Epoch: [217/300], Batch: [300/600]Discriminator Loss: 1.2332, Generator Loss: 0.4883\n",
      "Epoch: [217/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7549\n",
      "Epoch: [217/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5832\n",
      "Epoch: [217/300], Batch: [600/600]Discriminator Loss: 1.2096, Generator Loss: 0.4773\n",
      "Epoch: [217/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8469\n",
      "Epoch: [217/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6228\n",
      "Epoch: [218/300], Batch: [300/600]Discriminator Loss: 1.2001, Generator Loss: 0.4979\n",
      "Epoch: [218/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7728\n",
      "Epoch: [218/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5713\n",
      "Epoch: [218/300], Batch: [600/600]Discriminator Loss: 1.2246, Generator Loss: 0.5279\n",
      "Epoch: [218/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7406\n",
      "Epoch: [218/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5809\n",
      "Epoch: [219/300], Batch: [300/600]Discriminator Loss: 1.1695, Generator Loss: 0.5219\n",
      "Epoch: [219/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7504\n",
      "Epoch: [219/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5466\n",
      "Epoch: [219/300], Batch: [600/600]Discriminator Loss: 1.3251, Generator Loss: 0.5623\n",
      "Epoch: [219/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8515\n",
      "Epoch: [219/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6433\n",
      "Epoch: [220/300], Batch: [300/600]Discriminator Loss: 1.2083, Generator Loss: 0.5146\n",
      "Epoch: [220/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8326\n",
      "Epoch: [220/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6197\n",
      "Epoch: [220/300], Batch: [600/600]Discriminator Loss: 1.0939, Generator Loss: 0.4256\n",
      "Epoch: [220/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8013\n",
      "Epoch: [220/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5580\n",
      "Generated images at epoch 220\n",
      "Epoch: [221/300], Batch: [300/600]Discriminator Loss: 1.0956, Generator Loss: 0.4149\n",
      "Epoch: [221/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7382\n",
      "Epoch: [221/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5250\n",
      "Epoch: [221/300], Batch: [600/600]Discriminator Loss: 1.2633, Generator Loss: 0.5459\n",
      "Epoch: [221/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7673\n",
      "Epoch: [221/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6101\n",
      "Epoch: [222/300], Batch: [300/600]Discriminator Loss: 1.1215, Generator Loss: 0.5518\n",
      "Epoch: [222/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.6980\n",
      "Epoch: [222/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5136\n",
      "Epoch: [222/300], Batch: [600/600]Discriminator Loss: 1.2105, Generator Loss: 0.5053\n",
      "Epoch: [222/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7938\n",
      "Epoch: [222/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6032\n",
      "Epoch: [223/300], Batch: [300/600]Discriminator Loss: 1.2162, Generator Loss: 0.5546\n",
      "Epoch: [223/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7167\n",
      "Epoch: [223/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5455\n",
      "Epoch: [223/300], Batch: [600/600]Discriminator Loss: 1.2672, Generator Loss: 0.6470\n",
      "Epoch: [223/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8099\n",
      "Epoch: [223/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6342\n",
      "Epoch: [224/300], Batch: [300/600]Discriminator Loss: 1.2711, Generator Loss: 0.5922\n",
      "Epoch: [224/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8552\n",
      "Epoch: [224/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6514\n",
      "Epoch: [224/300], Batch: [600/600]Discriminator Loss: 1.2154, Generator Loss: 0.5278\n",
      "Epoch: [224/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7160\n",
      "Epoch: [224/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5637\n",
      "Epoch: [225/300], Batch: [300/600]Discriminator Loss: 1.3502, Generator Loss: 0.5540\n",
      "Epoch: [225/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8531\n",
      "Epoch: [225/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6625\n",
      "Epoch: [225/300], Batch: [600/600]Discriminator Loss: 1.2798, Generator Loss: 0.7408\n",
      "Epoch: [225/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8109\n",
      "Epoch: [225/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6146\n",
      "Epoch: [226/300], Batch: [300/600]Discriminator Loss: 1.1948, Generator Loss: 0.6062\n",
      "Epoch: [226/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7918\n",
      "Epoch: [226/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5919\n",
      "Epoch: [226/300], Batch: [600/600]Discriminator Loss: 1.1660, Generator Loss: 0.6753\n",
      "Epoch: [226/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7525\n",
      "Epoch: [226/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5710\n",
      "Epoch: [227/300], Batch: [300/600]Discriminator Loss: 1.3651, Generator Loss: 0.6043\n",
      "Epoch: [227/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8104\n",
      "Epoch: [227/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6558\n",
      "Epoch: [227/300], Batch: [600/600]Discriminator Loss: 1.3412, Generator Loss: 0.5013\n",
      "Epoch: [227/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8500\n",
      "Epoch: [227/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6610\n",
      "Epoch: [228/300], Batch: [300/600]Discriminator Loss: 1.1883, Generator Loss: 0.4772\n",
      "Epoch: [228/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8602\n",
      "Epoch: [228/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6227\n",
      "Epoch: [228/300], Batch: [600/600]Discriminator Loss: 1.2064, Generator Loss: 0.5616\n",
      "Epoch: [228/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8308\n",
      "Epoch: [228/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6149\n",
      "Epoch: [229/300], Batch: [300/600]Discriminator Loss: 1.2039, Generator Loss: 0.5484\n",
      "Epoch: [229/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7327\n",
      "Epoch: [229/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5715\n",
      "Epoch: [229/300], Batch: [600/600]Discriminator Loss: 1.2404, Generator Loss: 0.6661\n",
      "Epoch: [229/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8022\n",
      "Epoch: [229/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6046\n",
      "Epoch: [230/300], Batch: [300/600]Discriminator Loss: 1.1435, Generator Loss: 0.5841\n",
      "Epoch: [230/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7705\n",
      "Epoch: [230/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5490\n",
      "Epoch: [230/300], Batch: [600/600]Discriminator Loss: 1.1700, Generator Loss: 0.4501\n",
      "Epoch: [230/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7262\n",
      "Epoch: [230/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5471\n",
      "Generated images at epoch 230\n",
      "Epoch: [231/300], Batch: [300/600]Discriminator Loss: 1.1275, Generator Loss: 0.5893\n",
      "Epoch: [231/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7967\n",
      "Epoch: [231/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5651\n",
      "Epoch: [231/300], Batch: [600/600]Discriminator Loss: 1.2427, Generator Loss: 0.5472\n",
      "Epoch: [231/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7907\n",
      "Epoch: [231/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6113\n",
      "Epoch: [232/300], Batch: [300/600]Discriminator Loss: 1.2004, Generator Loss: 0.4210\n",
      "Epoch: [232/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7789\n",
      "Epoch: [232/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5903\n",
      "Epoch: [232/300], Batch: [600/600]Discriminator Loss: 1.2662, Generator Loss: 0.5568\n",
      "Epoch: [232/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7888\n",
      "Epoch: [232/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6139\n",
      "Epoch: [233/300], Batch: [300/600]Discriminator Loss: 1.1743, Generator Loss: 0.5993\n",
      "Epoch: [233/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7431\n",
      "Epoch: [233/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5574\n",
      "Epoch: [233/300], Batch: [600/600]Discriminator Loss: 1.1638, Generator Loss: 0.7097\n",
      "Epoch: [233/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8237\n",
      "Epoch: [233/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5850\n",
      "Epoch: [234/300], Batch: [300/600]Discriminator Loss: 1.3848, Generator Loss: 0.5952\n",
      "Epoch: [234/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8321\n",
      "Epoch: [234/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6787\n",
      "Epoch: [234/300], Batch: [600/600]Discriminator Loss: 1.2704, Generator Loss: 0.5517\n",
      "Epoch: [234/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8565\n",
      "Epoch: [234/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6504\n",
      "Epoch: [235/300], Batch: [300/600]Discriminator Loss: 1.2590, Generator Loss: 0.6127\n",
      "Epoch: [235/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7970\n",
      "Epoch: [235/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6108\n",
      "Epoch: [235/300], Batch: [600/600]Discriminator Loss: 1.3251, Generator Loss: 0.5024\n",
      "Epoch: [235/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8151\n",
      "Epoch: [235/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6554\n",
      "Epoch: [236/300], Batch: [300/600]Discriminator Loss: 1.2124, Generator Loss: 0.4677\n",
      "Epoch: [236/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7649\n",
      "Epoch: [236/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5839\n",
      "Epoch: [236/300], Batch: [600/600]Discriminator Loss: 1.2437, Generator Loss: 0.5545\n",
      "Epoch: [236/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7887\n",
      "Epoch: [236/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5928\n",
      "Epoch: [237/300], Batch: [300/600]Discriminator Loss: 1.2537, Generator Loss: 0.5789\n",
      "Epoch: [237/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8076\n",
      "Epoch: [237/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6144\n",
      "Epoch: [237/300], Batch: [600/600]Discriminator Loss: 1.1398, Generator Loss: 0.4728\n",
      "Epoch: [237/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8081\n",
      "Epoch: [237/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5819\n",
      "Epoch: [238/300], Batch: [300/600]Discriminator Loss: 1.3266, Generator Loss: 0.5015\n",
      "Epoch: [238/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8445\n",
      "Epoch: [238/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6583\n",
      "Epoch: [238/300], Batch: [600/600]Discriminator Loss: 1.2502, Generator Loss: 0.6381\n",
      "Epoch: [238/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8228\n",
      "Epoch: [238/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6157\n",
      "Epoch: [239/300], Batch: [300/600]Discriminator Loss: 1.2775, Generator Loss: 0.4290\n",
      "Epoch: [239/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7918\n",
      "Epoch: [239/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6242\n",
      "Epoch: [239/300], Batch: [600/600]Discriminator Loss: 1.3513, Generator Loss: 0.6302\n",
      "Epoch: [239/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8588\n",
      "Epoch: [239/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6513\n",
      "Epoch: [240/300], Batch: [300/600]Discriminator Loss: 1.3198, Generator Loss: 0.5564\n",
      "Epoch: [240/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8317\n",
      "Epoch: [240/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6554\n",
      "Epoch: [240/300], Batch: [600/600]Discriminator Loss: 1.1702, Generator Loss: 0.5480\n",
      "Epoch: [240/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7777\n",
      "Epoch: [240/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5718\n",
      "Generated images at epoch 240\n",
      "Epoch: [241/300], Batch: [300/600]Discriminator Loss: 1.4917, Generator Loss: 0.5854\n",
      "Epoch: [241/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8164\n",
      "Epoch: [241/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6906\n",
      "Epoch: [241/300], Batch: [600/600]Discriminator Loss: 1.2826, Generator Loss: 0.4801\n",
      "Epoch: [241/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7451\n",
      "Epoch: [241/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6112\n",
      "Epoch: [242/300], Batch: [300/600]Discriminator Loss: 1.1715, Generator Loss: 0.5443\n",
      "Epoch: [242/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7637\n",
      "Epoch: [242/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5778\n",
      "Epoch: [242/300], Batch: [600/600]Discriminator Loss: 1.1122, Generator Loss: 0.4832\n",
      "Epoch: [242/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8074\n",
      "Epoch: [242/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5615\n",
      "Epoch: [243/300], Batch: [300/600]Discriminator Loss: 1.2270, Generator Loss: 0.6461\n",
      "Epoch: [243/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8040\n",
      "Epoch: [243/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6091\n",
      "Epoch: [243/300], Batch: [600/600]Discriminator Loss: 1.3054, Generator Loss: 0.5627\n",
      "Epoch: [243/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8049\n",
      "Epoch: [243/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6418\n",
      "Epoch: [244/300], Batch: [300/600]Discriminator Loss: 1.4238, Generator Loss: 0.5478\n",
      "Epoch: [244/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7986\n",
      "Epoch: [244/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6718\n",
      "Epoch: [244/300], Batch: [600/600]Discriminator Loss: 1.5430, Generator Loss: 0.4918\n",
      "Epoch: [244/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8736\n",
      "Epoch: [244/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.7163\n",
      "Epoch: [245/300], Batch: [300/600]Discriminator Loss: 1.2514, Generator Loss: 0.5487\n",
      "Epoch: [245/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7557\n",
      "Epoch: [245/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5832\n",
      "Epoch: [245/300], Batch: [600/600]Discriminator Loss: 1.3167, Generator Loss: 0.5798\n",
      "Epoch: [245/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7997\n",
      "Epoch: [245/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6313\n",
      "Epoch: [246/300], Batch: [300/600]Discriminator Loss: 1.3289, Generator Loss: 0.4626\n",
      "Epoch: [246/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7924\n",
      "Epoch: [246/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6448\n",
      "Epoch: [246/300], Batch: [600/600]Discriminator Loss: 1.3055, Generator Loss: 0.4492\n",
      "Epoch: [246/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8268\n",
      "Epoch: [246/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6437\n",
      "Epoch: [247/300], Batch: [300/600]Discriminator Loss: 1.0978, Generator Loss: 0.4502\n",
      "Epoch: [247/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7764\n",
      "Epoch: [247/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5449\n",
      "Epoch: [247/300], Batch: [600/600]Discriminator Loss: 1.5991, Generator Loss: 0.5631\n",
      "Epoch: [247/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8701\n",
      "Epoch: [247/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.7439\n",
      "Epoch: [248/300], Batch: [300/600]Discriminator Loss: 1.2377, Generator Loss: 0.5713\n",
      "Epoch: [248/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8582\n",
      "Epoch: [248/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6284\n",
      "Epoch: [248/300], Batch: [600/600]Discriminator Loss: 1.1092, Generator Loss: 0.5067\n",
      "Epoch: [248/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8044\n",
      "Epoch: [248/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5667\n",
      "Epoch: [249/300], Batch: [300/600]Discriminator Loss: 1.3646, Generator Loss: 0.5325\n",
      "Epoch: [249/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8412\n",
      "Epoch: [249/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6696\n",
      "Epoch: [249/300], Batch: [600/600]Discriminator Loss: 1.2275, Generator Loss: 0.5506\n",
      "Epoch: [249/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8306\n",
      "Epoch: [249/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6111\n",
      "Epoch: [250/300], Batch: [300/600]Discriminator Loss: 1.3402, Generator Loss: 0.6719\n",
      "Epoch: [250/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8520\n",
      "Epoch: [250/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6534\n",
      "Epoch: [250/300], Batch: [600/600]Discriminator Loss: 1.2886, Generator Loss: 0.4903\n",
      "Epoch: [250/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8667\n",
      "Epoch: [250/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6509\n",
      "Generated images at epoch 250\n",
      "Epoch: [251/300], Batch: [300/600]Discriminator Loss: 1.2908, Generator Loss: 0.6357\n",
      "Epoch: [251/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8170\n",
      "Epoch: [251/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6319\n",
      "Epoch: [251/300], Batch: [600/600]Discriminator Loss: 1.2355, Generator Loss: 0.3797\n",
      "Epoch: [251/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8272\n",
      "Epoch: [251/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6172\n",
      "Epoch: [252/300], Batch: [300/600]Discriminator Loss: 1.3922, Generator Loss: 0.5368\n",
      "Epoch: [252/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8327\n",
      "Epoch: [252/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6430\n",
      "Epoch: [252/300], Batch: [600/600]Discriminator Loss: 1.1678, Generator Loss: 0.5605\n",
      "Epoch: [252/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8151\n",
      "Epoch: [252/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5970\n",
      "Epoch: [253/300], Batch: [300/600]Discriminator Loss: 1.1928, Generator Loss: 0.4892\n",
      "Epoch: [253/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7803\n",
      "Epoch: [253/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5916\n",
      "Epoch: [253/300], Batch: [600/600]Discriminator Loss: 1.3675, Generator Loss: 0.5431\n",
      "Epoch: [253/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8064\n",
      "Epoch: [253/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6723\n",
      "Epoch: [254/300], Batch: [300/600]Discriminator Loss: 1.1990, Generator Loss: 0.5874\n",
      "Epoch: [254/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8471\n",
      "Epoch: [254/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5942\n",
      "Epoch: [254/300], Batch: [600/600]Discriminator Loss: 1.1173, Generator Loss: 0.5717\n",
      "Epoch: [254/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8049\n",
      "Epoch: [254/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5730\n",
      "Epoch: [255/300], Batch: [300/600]Discriminator Loss: 1.1787, Generator Loss: 0.5073\n",
      "Epoch: [255/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7658\n",
      "Epoch: [255/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5818\n",
      "Epoch: [255/300], Batch: [600/600]Discriminator Loss: 1.3776, Generator Loss: 0.6299\n",
      "Epoch: [255/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8479\n",
      "Epoch: [255/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6733\n",
      "Epoch: [256/300], Batch: [300/600]Discriminator Loss: 1.2192, Generator Loss: 0.5419\n",
      "Epoch: [256/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8368\n",
      "Epoch: [256/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6116\n",
      "Epoch: [256/300], Batch: [600/600]Discriminator Loss: 1.1233, Generator Loss: 0.5821\n",
      "Epoch: [256/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7660\n",
      "Epoch: [256/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5521\n",
      "Epoch: [257/300], Batch: [300/600]Discriminator Loss: 1.2076, Generator Loss: 0.5521\n",
      "Epoch: [257/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8230\n",
      "Epoch: [257/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6114\n",
      "Epoch: [257/300], Batch: [600/600]Discriminator Loss: 1.1861, Generator Loss: 0.5837\n",
      "Epoch: [257/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8205\n",
      "Epoch: [257/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5864\n",
      "Epoch: [258/300], Batch: [300/600]Discriminator Loss: 1.1378, Generator Loss: 0.6233\n",
      "Epoch: [258/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8032\n",
      "Epoch: [258/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5681\n",
      "Epoch: [258/300], Batch: [600/600]Discriminator Loss: 1.3838, Generator Loss: 0.3968\n",
      "Epoch: [258/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8420\n",
      "Epoch: [258/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6763\n",
      "Epoch: [259/300], Batch: [300/600]Discriminator Loss: 1.2299, Generator Loss: 0.6831\n",
      "Epoch: [259/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8279\n",
      "Epoch: [259/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5892\n",
      "Epoch: [259/300], Batch: [600/600]Discriminator Loss: 1.1706, Generator Loss: 0.8027\n",
      "Epoch: [259/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8446\n",
      "Epoch: [259/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6035\n",
      "Epoch: [260/300], Batch: [300/600]Discriminator Loss: 1.1413, Generator Loss: 0.5548\n",
      "Epoch: [260/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7631\n",
      "Epoch: [260/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5430\n",
      "Epoch: [260/300], Batch: [600/600]Discriminator Loss: 1.2006, Generator Loss: 0.4163\n",
      "Epoch: [260/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7488\n",
      "Epoch: [260/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5682\n",
      "Generated images at epoch 260\n",
      "Epoch: [261/300], Batch: [300/600]Discriminator Loss: 1.3350, Generator Loss: 0.5569\n",
      "Epoch: [261/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8572\n",
      "Epoch: [261/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6634\n",
      "Epoch: [261/300], Batch: [600/600]Discriminator Loss: 1.2904, Generator Loss: 0.4109\n",
      "Epoch: [261/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7915\n",
      "Epoch: [261/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6250\n",
      "Epoch: [262/300], Batch: [300/600]Discriminator Loss: 1.2937, Generator Loss: 0.4497\n",
      "Epoch: [262/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7931\n",
      "Epoch: [262/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6179\n",
      "Epoch: [262/300], Batch: [600/600]Discriminator Loss: 1.1550, Generator Loss: 0.6928\n",
      "Epoch: [262/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7894\n",
      "Epoch: [262/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5758\n",
      "Epoch: [263/300], Batch: [300/600]Discriminator Loss: 1.3775, Generator Loss: 0.6778\n",
      "Epoch: [263/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8247\n",
      "Epoch: [263/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6513\n",
      "Epoch: [263/300], Batch: [600/600]Discriminator Loss: 1.2236, Generator Loss: 0.6058\n",
      "Epoch: [263/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8530\n",
      "Epoch: [263/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6325\n",
      "Epoch: [264/300], Batch: [300/600]Discriminator Loss: 1.2545, Generator Loss: 0.7009\n",
      "Epoch: [264/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8171\n",
      "Epoch: [264/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6314\n",
      "Epoch: [264/300], Batch: [600/600]Discriminator Loss: 1.0864, Generator Loss: 0.5566\n",
      "Epoch: [264/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7798\n",
      "Epoch: [264/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5365\n",
      "Epoch: [265/300], Batch: [300/600]Discriminator Loss: 1.2449, Generator Loss: 0.4821\n",
      "Epoch: [265/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7703\n",
      "Epoch: [265/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6113\n",
      "Epoch: [265/300], Batch: [600/600]Discriminator Loss: 1.2510, Generator Loss: 0.5275\n",
      "Epoch: [265/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8109\n",
      "Epoch: [265/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6287\n",
      "Epoch: [266/300], Batch: [300/600]Discriminator Loss: 1.1632, Generator Loss: 0.4026\n",
      "Epoch: [266/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7904\n",
      "Epoch: [266/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5739\n",
      "Epoch: [266/300], Batch: [600/600]Discriminator Loss: 0.9511, Generator Loss: 0.5840\n",
      "Epoch: [266/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7750\n",
      "Epoch: [266/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.4700\n",
      "Epoch: [267/300], Batch: [300/600]Discriminator Loss: 1.0308, Generator Loss: 0.4764\n",
      "Epoch: [267/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8187\n",
      "Epoch: [267/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5395\n",
      "Epoch: [267/300], Batch: [600/600]Discriminator Loss: 1.1646, Generator Loss: 0.4837\n",
      "Epoch: [267/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.6712\n",
      "Epoch: [267/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5158\n",
      "Epoch: [268/300], Batch: [300/600]Discriminator Loss: 1.3307, Generator Loss: 0.4522\n",
      "Epoch: [268/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7284\n",
      "Epoch: [268/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6168\n",
      "Epoch: [268/300], Batch: [600/600]Discriminator Loss: 1.2970, Generator Loss: 0.6037\n",
      "Epoch: [268/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7792\n",
      "Epoch: [268/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6226\n",
      "Epoch: [269/300], Batch: [300/600]Discriminator Loss: 1.0684, Generator Loss: 0.5277\n",
      "Epoch: [269/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7221\n",
      "Epoch: [269/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.4995\n",
      "Epoch: [269/300], Batch: [600/600]Discriminator Loss: 1.4315, Generator Loss: 0.5280\n",
      "Epoch: [269/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8202\n",
      "Epoch: [269/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6955\n",
      "Epoch: [270/300], Batch: [300/600]Discriminator Loss: 1.0471, Generator Loss: 0.6129\n",
      "Epoch: [270/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7832\n",
      "Epoch: [270/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5188\n",
      "Epoch: [270/300], Batch: [600/600]Discriminator Loss: 1.1483, Generator Loss: 0.6524\n",
      "Epoch: [270/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7668\n",
      "Epoch: [270/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5590\n",
      "Generated images at epoch 270\n",
      "Epoch: [271/300], Batch: [300/600]Discriminator Loss: 1.1195, Generator Loss: 0.5529\n",
      "Epoch: [271/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7825\n",
      "Epoch: [271/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5578\n",
      "Epoch: [271/300], Batch: [600/600]Discriminator Loss: 1.1884, Generator Loss: 0.5315\n",
      "Epoch: [271/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7302\n",
      "Epoch: [271/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5632\n",
      "Epoch: [272/300], Batch: [300/600]Discriminator Loss: 1.1615, Generator Loss: 0.6234\n",
      "Epoch: [272/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7639\n",
      "Epoch: [272/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5606\n",
      "Epoch: [272/300], Batch: [600/600]Discriminator Loss: 1.1689, Generator Loss: 0.4081\n",
      "Epoch: [272/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7399\n",
      "Epoch: [272/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5520\n",
      "Epoch: [273/300], Batch: [300/600]Discriminator Loss: 1.1607, Generator Loss: 0.5436\n",
      "Epoch: [273/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8288\n",
      "Epoch: [273/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5864\n",
      "Epoch: [273/300], Batch: [600/600]Discriminator Loss: 1.1782, Generator Loss: 0.4715\n",
      "Epoch: [273/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7714\n",
      "Epoch: [273/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5692\n",
      "Epoch: [274/300], Batch: [300/600]Discriminator Loss: 1.1144, Generator Loss: 0.5132\n",
      "Epoch: [274/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7062\n",
      "Epoch: [274/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5186\n",
      "Epoch: [274/300], Batch: [600/600]Discriminator Loss: 1.3602, Generator Loss: 0.4923\n",
      "Epoch: [274/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8033\n",
      "Epoch: [274/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6615\n",
      "Epoch: [275/300], Batch: [300/600]Discriminator Loss: 1.2668, Generator Loss: 0.6573\n",
      "Epoch: [275/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8622\n",
      "Epoch: [275/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6434\n",
      "Epoch: [275/300], Batch: [600/600]Discriminator Loss: 1.2777, Generator Loss: 0.4807\n",
      "Epoch: [275/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8037\n",
      "Epoch: [275/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6411\n",
      "Epoch: [276/300], Batch: [300/600]Discriminator Loss: 1.2650, Generator Loss: 0.4473\n",
      "Epoch: [276/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7912\n",
      "Epoch: [276/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6189\n",
      "Epoch: [276/300], Batch: [600/600]Discriminator Loss: 1.2432, Generator Loss: 0.3959\n",
      "Epoch: [276/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8440\n",
      "Epoch: [276/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6427\n",
      "Epoch: [277/300], Batch: [300/600]Discriminator Loss: 1.2426, Generator Loss: 0.5782\n",
      "Epoch: [277/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8261\n",
      "Epoch: [277/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6172\n",
      "Epoch: [277/300], Batch: [600/600]Discriminator Loss: 1.1662, Generator Loss: 0.5875\n",
      "Epoch: [277/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8173\n",
      "Epoch: [277/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5920\n",
      "Epoch: [278/300], Batch: [300/600]Discriminator Loss: 1.1121, Generator Loss: 0.5934\n",
      "Epoch: [278/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8488\n",
      "Epoch: [278/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5817\n",
      "Epoch: [278/300], Batch: [600/600]Discriminator Loss: 1.1511, Generator Loss: 0.5529\n",
      "Epoch: [278/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8311\n",
      "Epoch: [278/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5954\n",
      "Epoch: [279/300], Batch: [300/600]Discriminator Loss: 1.1345, Generator Loss: 0.4708\n",
      "Epoch: [279/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8240\n",
      "Epoch: [279/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5852\n",
      "Epoch: [279/300], Batch: [600/600]Discriminator Loss: 1.2119, Generator Loss: 0.5350\n",
      "Epoch: [279/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7820\n",
      "Epoch: [279/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6031\n",
      "Epoch: [280/300], Batch: [300/600]Discriminator Loss: 1.3014, Generator Loss: 0.5073\n",
      "Epoch: [280/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8478\n",
      "Epoch: [280/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6322\n",
      "Epoch: [280/300], Batch: [600/600]Discriminator Loss: 1.2950, Generator Loss: 0.5097\n",
      "Epoch: [280/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8111\n",
      "Epoch: [280/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6389\n",
      "Generated images at epoch 280\n",
      "Epoch: [281/300], Batch: [300/600]Discriminator Loss: 1.2205, Generator Loss: 0.4516\n",
      "Epoch: [281/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7514\n",
      "Epoch: [281/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5867\n",
      "Epoch: [281/300], Batch: [600/600]Discriminator Loss: 1.1347, Generator Loss: 0.4608\n",
      "Epoch: [281/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7474\n",
      "Epoch: [281/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5497\n",
      "Epoch: [282/300], Batch: [300/600]Discriminator Loss: 1.1677, Generator Loss: 0.5254\n",
      "Epoch: [282/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8127\n",
      "Epoch: [282/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5926\n",
      "Epoch: [282/300], Batch: [600/600]Discriminator Loss: 1.0971, Generator Loss: 0.6499\n",
      "Epoch: [282/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8167\n",
      "Epoch: [282/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5491\n",
      "Epoch: [283/300], Batch: [300/600]Discriminator Loss: 1.1647, Generator Loss: 0.5977\n",
      "Epoch: [283/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8209\n",
      "Epoch: [283/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5951\n",
      "Epoch: [283/300], Batch: [600/600]Discriminator Loss: 1.2625, Generator Loss: 0.5476\n",
      "Epoch: [283/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8313\n",
      "Epoch: [283/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6382\n",
      "Epoch: [284/300], Batch: [300/600]Discriminator Loss: 1.1835, Generator Loss: 0.4297\n",
      "Epoch: [284/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7914\n",
      "Epoch: [284/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5843\n",
      "Epoch: [284/300], Batch: [600/600]Discriminator Loss: 1.0822, Generator Loss: 0.5024\n",
      "Epoch: [284/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7631\n",
      "Epoch: [284/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5343\n",
      "Epoch: [285/300], Batch: [300/600]Discriminator Loss: 1.2622, Generator Loss: 0.5337\n",
      "Epoch: [285/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8429\n",
      "Epoch: [285/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6298\n",
      "Epoch: [285/300], Batch: [600/600]Discriminator Loss: 1.2248, Generator Loss: 0.4930\n",
      "Epoch: [285/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7053\n",
      "Epoch: [285/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5559\n",
      "Epoch: [286/300], Batch: [300/600]Discriminator Loss: 1.3500, Generator Loss: 0.4939\n",
      "Epoch: [286/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7940\n",
      "Epoch: [286/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6413\n",
      "Epoch: [286/300], Batch: [600/600]Discriminator Loss: 1.1793, Generator Loss: 0.5840\n",
      "Epoch: [286/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8333\n",
      "Epoch: [286/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6078\n",
      "Epoch: [287/300], Batch: [300/600]Discriminator Loss: 1.3249, Generator Loss: 0.6469\n",
      "Epoch: [287/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8381\n",
      "Epoch: [287/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6463\n",
      "Epoch: [287/300], Batch: [600/600]Discriminator Loss: 1.2456, Generator Loss: 0.6800\n",
      "Epoch: [287/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8189\n",
      "Epoch: [287/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6268\n",
      "Epoch: [288/300], Batch: [300/600]Discriminator Loss: 1.3043, Generator Loss: 0.5171\n",
      "Epoch: [288/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7993\n",
      "Epoch: [288/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6460\n",
      "Epoch: [288/300], Batch: [600/600]Discriminator Loss: 1.2456, Generator Loss: 0.5094\n",
      "Epoch: [288/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8168\n",
      "Epoch: [288/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6097\n",
      "Epoch: [289/300], Batch: [300/600]Discriminator Loss: 1.2647, Generator Loss: 0.6387\n",
      "Epoch: [289/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7631\n",
      "Epoch: [289/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5933\n",
      "Epoch: [289/300], Batch: [600/600]Discriminator Loss: 1.1558, Generator Loss: 0.3989\n",
      "Epoch: [289/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8129\n",
      "Epoch: [289/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5815\n",
      "Epoch: [290/300], Batch: [300/600]Discriminator Loss: 1.3012, Generator Loss: 0.5107\n",
      "Epoch: [290/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8282\n",
      "Epoch: [290/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6369\n",
      "Epoch: [290/300], Batch: [600/600]Discriminator Loss: 1.2063, Generator Loss: 0.5773\n",
      "Epoch: [290/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7356\n",
      "Epoch: [290/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5737\n",
      "Generated images at epoch 290\n",
      "Epoch: [291/300], Batch: [300/600]Discriminator Loss: 1.1609, Generator Loss: 0.7056\n",
      "Epoch: [291/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7879\n",
      "Epoch: [291/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5655\n",
      "Epoch: [291/300], Batch: [600/600]Discriminator Loss: 1.3690, Generator Loss: 0.5293\n",
      "Epoch: [291/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8082\n",
      "Epoch: [291/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6500\n",
      "Epoch: [292/300], Batch: [300/600]Discriminator Loss: 1.2653, Generator Loss: 0.6201\n",
      "Epoch: [292/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8362\n",
      "Epoch: [292/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6040\n",
      "Epoch: [292/300], Batch: [600/600]Discriminator Loss: 1.3004, Generator Loss: 0.4530\n",
      "Epoch: [292/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7661\n",
      "Epoch: [292/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6225\n",
      "Epoch: [293/300], Batch: [300/600]Discriminator Loss: 1.0612, Generator Loss: 0.5157\n",
      "Epoch: [293/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7707\n",
      "Epoch: [293/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5108\n",
      "Epoch: [293/300], Batch: [600/600]Discriminator Loss: 1.2761, Generator Loss: 0.6080\n",
      "Epoch: [293/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8019\n",
      "Epoch: [293/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6299\n",
      "Epoch: [294/300], Batch: [300/600]Discriminator Loss: 1.3493, Generator Loss: 0.5652\n",
      "Epoch: [294/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8175\n",
      "Epoch: [294/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.6567\n",
      "Epoch: [294/300], Batch: [600/600]Discriminator Loss: 1.1851, Generator Loss: 0.6019\n",
      "Epoch: [294/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8222\n",
      "Epoch: [294/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5987\n",
      "Epoch: [295/300], Batch: [300/600]Discriminator Loss: 1.1723, Generator Loss: 0.6528\n",
      "Epoch: [295/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8125\n",
      "Epoch: [295/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5758\n",
      "Epoch: [295/300], Batch: [600/600]Discriminator Loss: 1.2732, Generator Loss: 0.6335\n",
      "Epoch: [295/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8102\n",
      "Epoch: [295/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6286\n",
      "Epoch: [296/300], Batch: [300/600]Discriminator Loss: 1.1246, Generator Loss: 0.5248\n",
      "Epoch: [296/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8135\n",
      "Epoch: [296/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5534\n",
      "Epoch: [296/300], Batch: [600/600]Discriminator Loss: 1.0552, Generator Loss: 0.5631\n",
      "Epoch: [296/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7904\n",
      "Epoch: [296/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5361\n",
      "Epoch: [297/300], Batch: [300/600]Discriminator Loss: 1.3051, Generator Loss: 0.4887\n",
      "Epoch: [297/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7617\n",
      "Epoch: [297/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5964\n",
      "Epoch: [297/300], Batch: [600/600]Discriminator Loss: 1.1197, Generator Loss: 0.5563\n",
      "Epoch: [297/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7702\n",
      "Epoch: [297/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5423\n",
      "Epoch: [298/300], Batch: [300/600]Discriminator Loss: 1.0637, Generator Loss: 0.5567\n",
      "Epoch: [298/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8035\n",
      "Epoch: [298/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5480\n",
      "Epoch: [298/300], Batch: [600/600]Discriminator Loss: 1.2057, Generator Loss: 0.3251\n",
      "Epoch: [298/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7447\n",
      "Epoch: [298/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5758\n",
      "Epoch: [299/300], Batch: [300/600]Discriminator Loss: 1.1000, Generator Loss: 0.4481\n",
      "Epoch: [299/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.8213\n",
      "Epoch: [299/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5730\n",
      "Epoch: [299/300], Batch: [600/600]Discriminator Loss: 1.2056, Generator Loss: 0.4705\n",
      "Epoch: [299/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.7761\n",
      "Epoch: [299/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.5955\n",
      "Epoch: [300/300], Batch: [300/600]Discriminator Loss: 1.1661, Generator Loss: 0.4776\n",
      "Epoch: [300/300], Batch: [300/600]Real sample score for Discriminator D(x): 0.7739\n",
      "Epoch: [300/300], Batch: [300/600]Fake sample score for Discriminator D(G(x)): 0.5676\n",
      "Epoch: [300/300], Batch: [600/600]Discriminator Loss: 1.1809, Generator Loss: 0.5166\n",
      "Epoch: [300/300], Batch: [600/600]Real sample score for Discriminator D(x): 0.8612\n",
      "Epoch: [300/300], Batch: [600/600]Fake sample score for Discriminator D(G(x)): 0.6094\n",
      "Generated images at epoch 300\n"
     ]
    }
   ],
   "source": [
    "from training import run_epoch, generate_and_save_images\n",
    "\n",
    "image_prefix = \"./sample\"\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    run_epoch(d_net=D, g_net=G, \n",
    "              train_loader=data_loader, criterion=criterion, \n",
    "              d_optim=d_optim, g_optim=g_optim,\n",
    "              batch_size=batch_size, latent_size=latent_size, device=device,\n",
    "              d_loss_list=d_loss_list, g_loss_list=g_loss_list,\n",
    "              real_score_list=real_score_list, fake_score_list=fake_score_list, \n",
    "              epoch=epoch, num_epochs=num_epochs)\n",
    "    if (epoch+1) % 10 == 0:\n",
    "        if generate_and_save_images(g_net=G, batch_size=batch_size, \n",
    "                                 latent_size=latent_size, device=device, \n",
    "                                 image_prefix=image_prefix, index=epoch+1):\n",
    "\n",
    "            print(f\"Generated images at epoch {epoch+1}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71fbd7a808a64921",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "# 6. 保存checkpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a5638a6e1e45ae2a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-04T03:14:18.891668Z",
     "start_time": "2024-03-04T03:14:18.874259Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "checkpoint_path = \"./checkpoints\"\n",
    "\n",
    "if not os.path.exists(checkpoint_path):\n",
    "    os.makedirs(checkpoint_path)\n",
    "torch.save(G.state_dict(), os.path.join(checkpoint_path, \"G.pt\"))\n",
    "torch.save(D.state_dict(), os.path.join(checkpoint_path, \"D.pt\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8e362f338b5052b",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "# 7. 检查训练结果\n",
    "## 损失变化与判别器评判分数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f8158de7fdfb0657",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-04T06:39:46.452023Z",
     "start_time": "2024-03-04T06:39:46.363427Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(d_loss_list[::200], label=\"Discriminator Loss\")\n",
    "plt.plot(g_loss_list[::200], label=\"Generator Loss\")\n",
    "plt.xlabel(\"Step\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend(loc='upper right', bbox_to_anchor=(1, 1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "eb0231cb99bb80ac",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-04T06:40:26.881713Z",
     "start_time": "2024-03-04T06:40:26.781017Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(real_score_list[::200], label=\"Real Score\")\n",
    "plt.plot(fake_score_list[::200], label=\"Fake Score\")\n",
    "plt.xlabel(\"Step\")\n",
    "plt.ylabel(\"Score\")\n",
    "plt.legend(loc='upper right', bbox_to_anchor=(1, 1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e395685c4d789522",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "## 生成的图像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bbdb3ac939da5b12",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-04T06:44:16.252046Z",
     "start_time": "2024-03-04T06:44:16.242401Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wl4HIpR5GjmSBrVAoJBIJ1IqsSEGGuRIIMTWAWSQSwcoWvE+4zo3eiSI5HiVx9YQDrVA0f4+zIz5PAb4UjTK0xJKWuCzB5IYxrqurCwgIYBhGq9Wq1WqNRhMaGmoymRISEjp16hQcHMyybGRkZI8ePRQKBar3sYD+B8JAC025ILndbrlcDpNhTU0NiKXJZIKKGY3G8PDwkJCQ4ODgL7/8MisrKyIiIjAwEIQEGgT2fmKx2GAwVFVVcVtJJpOxLCuTyWpra0GSzWYz7IrlcrlarTaZTGFhYS+//HJSUlJ6enpERAQMUnAjTI8sy8rlcrLDRN6qbeC2goICYu1qtVqhuelCpVJdvnzZaDTyd/cim2ka9boFgNeG6ndZPvNUVlZCBotr165B/FK73U46XzPMRHUBgBlGIpEoFAqHw6FWq2NjY10uV1VVlUQiMZlMEomkuLi4pKSkoqJCLBbfuHEDOm5dXR1R5/AxnfHYanLXeNyL5McwlBgMhiFDhhQVFcXGxo4cObJLly4KhaKsrEyj0WzcuDEpKenHH3/csmXLmTNnampqYIcpk8ngb11dHYgQYYYWg7WuUqmEIUmhUAwcOHDSpEl6vf7SpUtt27YtKCiw2+0HDx7Mzc399ttvIWyPzWaTy+VOpxM23gzDALMXsyJ5fq7VOXU5hIaurq42Go3l5eX82eADlbnxvffe40/SKJqqHlFy+CaH3F4Lcojq4wij+lkOcsi1sG7wuuVyOSw+dTrdjRs3ysvLXS5XYGCgUqm02WxOp7N///46nc5gMNjtdtg3guaGBGL19kEQQqGhoQzDrF69mmXZRx99lFxvVKrJKpplWYVCERgYaDQa27RpExER0a9fv/DwcAgYqVAoKioqxowZ06ZNm379+k2aNCkxMREmfFJhj1wJpK1g6avRaCQSSWxsbHV1dWpqaocOHWCQCgkJsdlsCQkJbdq0GTduXHR09JgxYwIDA2FccDgcTqcTdLaE2cfgyj7c1TyI1S9Xp8Q/1S7dNEwzZ84k+3hyHfZLPNukKWHgH98NmJvZZRkMhhaysSxrtVpjYmJsNpvL5bLb7Wq1Ojw8PDU1dcSIEXFxcRBm6uTJk9XV1RUVFWq1OiQkJC4uTiaTKRSKjIwMkjCrUf6GjcC9AmI8Z86cM2fOTJw4kUijRCKJjIz0uBGUNBqNBrSgn3zyicvl2rNnz/Dhw7t06RIZGVlVVVVRUVFVVXXt2rUHH3zw999/v3Tp0u7du++99144p5HJZHK5XCaTORwOpVJJnKFhQwg7XqvVOmLECIfDcfLkyREjRtx3331t2rSpq6u7dOlSTU3NuXPnXn/99ZycnPPnz9fW1qanp0ulUqVSCZtq6DYSiYQw+7JAbbTVfIbBYCADc2ho6Pr16zMyMkhxcrl8yJAhfDJmNjqk8SFZvXr1vHnzEEI6nc5j3qa7DE5OTia+wj4wFxcXBwYGNvXt9OnTP/jgg5Ywc/U0sLcEJWFISEh6evrPP//ctWtXhmGsVmt5efnJkyfDwsICAgJUKtW+ffuCgoLq6uqqqqqcTqdKpbJarbAYa37k8tD0eKhMQK3SPAMsHeEsQSqVZmVlqVQqi8Vit9sHDRqUkJBQUFCwa9eu77777sqVKyKRaMCAAYsWLdLpdJ07d4ans9lsbrdboVDY7XZ4dhjOiKIYtKYRERF2u12pVJrNZqVS+fDDD58+fVosFq9evbqqqsrtdj/00ENdu3YNCgoaNGhQaGhoUVFRRUWFy+XS6XTggA5q1eZfQSPA9fD6zqabDD688847CCGpVLpgwQLYZMO5cEO/L29B6szTsNhgMHjElcF/DR9MfbFwmzDD7ghWVhKJBDSBKpUqNTV1xYoVJpOpXbt2ffv2bdeu3cSJE4cOHRoSEhIUFDR06NDMzMyEhISoqCipVBoYGCiVSmHT2JLUESzLjhs3bvTo0Qih9957b9WqVQghhUIBypKgoKBmRhmEEJQll8vFYnH79u2NRqNMJuvWrVtSUtKUKVOysrJiY2NNJpNMJgNdy7Zt2yZNmrR58+bk5OQpU6YEBwfDsYdSqWRZFn4JzGKxGDSfEokkLi6OfEhJSbn77rvHjBkTEBAQFRUll8slEolSqVyxYkVaWtr333/ft2/fKVOmBAQEiEQikUik0+lYltXpdITZl95Jd+wfN25cYWHhnDlzEELXrl178sknYSCUSqUREREU81XxXKyWlZXBUS+5wjDMH3/8wf2XD3/rANrZK8AoRoxIQBpFItGNGzdWrFgREhKSn58/YMAAnU4nlUpv3LhRW1vbvn17lmVPnz4tlUorKiokEklJSQmsMB0Ox00tBIYMGfLCCy98/vnnubm5paWlc+bMmT9//ieffHLq1KmqqiqFQvHjjz9C5tOm4HQ6GYax2WxKpdJqtcbGxspksvz8/L59+2ZnZxcWFlqt1tTUVIyxRqOZN29ebW1teHh4XV1dYGDgr7/+CkEuEUIwJVZWVhKdCKyxQelSWloqkUiAWafTnThx4ujRo6GhoUFBQRhjvV4/bdq0S5cuKRSK06dPBwUFZWdn22w20HuBYYDVaiUDk9e9h6cezwMNlx9E5aVSqaqrq6mUIsS6uhXIhWA+dOhQly5dvGLmKipggQqrPoPBAIYskyZNOnfuXExMzNq1a0FHYrPZCgoKgoODr127xrJsWFjY+fPnUf2WlWt80xCwPnzggQcOHDhQUFBA+oBEIvnkk0/uu+8+p9P5xRdfvPTSS+fPn5dIJI0KNmiVUL3qQaPR1NbWMgwTHh6ekpKya9eugICAcePG/frrr3V1dStXroRd0qxZs9Rq9YULF0JDQ/Py8qqqqmBF4HK5QNvZkJn81Wg0iYmJubm5Go1m8uTJ69evl8vlS5YsAW3z6tWrg4KCvv/+e8j5YbVaYZfodrtJP/fiHdfU1JDlIpXOwd0SAGAngBBSq9W0cru3ghwSew6KzETvj2jX3NsGUSgUVqsVVmtgBOd2u5OSkgoKCsxms1wuf+KJJz799NPS0lKGYWpra2tra/v37//LL7/k5eWBntBjwG1+YYwx/uGHHwYOHNihQ4czZ86Qc0jQpigUClwfJ6oZeTYYDOXl5QzDgObGbrfb7Xaz2VxTU6PX6yMiIl555ZWqqqpz585Nnz69vLw8Pz9fqVSuXLly3bp1CoUCzjPgpB5jLBaLIUQyQshisVy9ehXUp263G2oFgd4CAwNVKtXSpUtZlv3jjz8efPDBqqqqU6dOdezY8eOPP169ejVCiJgBkqNawuzFApWEV6HVMxo2JdSPYZiXX36ZShFCg2EYWKJQj/AVGhpKiqBI68OiBswpEUIgkAihwMDA8+fPy+VylmVHjhxZU1Oj0WiWLl0aFhZWWFgYFBR08uTJMWPGjBgxQqfTBQQEwBYRNTb4Nqze9u3b9Xo99OaAgACE0KpVq9q1a9e/f3/QJ8P6sPkFakVFBch/XV1dXV2dw+GIjo4uKyszGAwsy06YMKGmpqauri4zM7OmpqampiY4OPjq1avPPvtscnKyVquFA22YuEjSBMDVq1ehlwItxhimNZVKVVdX165du7CwsLKysgceeADKioqKOnz48KhRo2JiYkg/gcUFTOn/W3R49VYEmmHeeeedOXPm4HpTHrqO24LOisIBC2CZgDmm2C1nJjYA5KROo9E4HI7evXvn5+e//PLLIGzDhw/v3r17eXl5RkbGAw880KNHD5PJdP78+YSEhIqKipqaGm7EyqZmxTNnzsTExJCzouXLlz/66KNwCA4dVyKRZGdn/+tf/9q8ebPNZrPb7Y0uUImKlcyfcEWr1QYHB2dkZLz88stKpbKgoODw4cNJSUm//vprSkrK2rVrd+zYUVhYaLFYioqKwDaInDESA25ghgkT1q6wpYqNjY2Pj3/uuefcbvfJkydLS0sVCoVcLpfL5Vu3bt2+fXtJSYler6+srAQbV1S/Vv8vYYvfo4CYM2cO7CKioqLAbEqI/I2CwmazUZwYqatMAT7IIeJMpGKxGKYIUG5nZ2cnJycrlcqjR4+mpqZ27tz51KlTtbW1arV637599957b3Z2dq9evTZu3OhyuWpqaogwN/OADocjNzcXwvUjhP7xj39A0SUlJRqN5ueffx48eHBKSopSqYS9X1P9BFaVID8MwxBb06qqqoyMjGHDhk2aNMlisRw7dqyqqgqsx3744YegoKC33377mWeeOXv2LGwXEUIajaauro7kFMX11uEwKoFdDmgZJRLJjBkzFi9e3KVLl4sXL/7555/5+fkhISHvvvtucXHxiBEj/t//+3+1tbXR0dFnz55FCMFiGw5pUeubgxObD4Lhw4cz9VaOly5dOnTo0B0nhxqNhpYcYoyTk5OpUDVk7ty5M3xuWNtmCgVhAPVpXV1dmzZtIHpydXX1+fPnd+3a9c0338ydO9dqtRYXF7vd7gkTJgQHBwcFBclksr179wYGBoIeErbTzRwJvvfee5999hmEbIRkPmq1WiQSFRYWpqSkqNXqAQMG5OXl9evX76bZyxmOx6DL5brvvvugAmKx+KeffnrqqaeOHz++bt26s2fP5uTkKJXK9957z+FwVFdXb9269erVq1FRUSUlJURXTMy7gZnMwy6XKz4+HmRSLBZfvHhx/vz5hYWFq1ev3rFjx5kzZ2Qy2dtvv61Wq41G459//imVSkEtBOZKGo2GuG54jUYPo/gM4QzDVFVVkVNUIWYD4ZiFS71Mvc5czuXLlze6BWiqONg1wZwGx2UajSYmJkav18fGxv773/8eOXJkVFQUWDwHBQV16dLllVdeeemll0aOHLlly5b4+HidTqfRaMRiMTDctPNJpdKBAwd6GI7DqrWqqqq4uBhEuhkGkUgE3V0kEoH3MAz3cEWlUoG/BfwYzvdWrVp17dq1N954Y+HChcHBwZGRkWDUJpVK9Xo9MfwSi8VKpRIOV6VSqageYrFYoVAYDAaJRAJnhvCDkJCQ9evX79y5s3fv3h07dlSr1VqtViKRiMVimUym1WoJ8y3eK7pcrvz8fMjjOW3atM8//5x6dDahDxso0nKFQSCtqQ/MZFaEsR86rlgs7t27d25ubnx8/MWLFwsKCkpKSsLCwoqLi10uV6dOnWJiYsxm8zvvvGM2m3NycojjP8uyLfTiJzpM2FZJJJKhQ4du3boVbDibZwCpA1FRKBS1tbU1NTVarZZl2ZqamsjIyIKCAswJOi6VSpOSkiZOnFhWVvbdd98xDHPx4kU4eICBA9yLUb21DSwrIKDBjRs31Gp1cHBwUVFR27ZtL168yLIsnEK7XC6NRtOzZ8+OHTvm5OTs27dPIpFcvnyZu76F80/k2wKVZdnExEQfbiRQqVSwn2YYhuTT/eyzz+6gKIlcOaSiZxJof4h4SzjxC4HtFuxthg4dumvXLqPRePDgQb1en5qaGhwcnJOTwzBM9+7dwenhwoULSqWysLAQdpigOyHnVc0DjEhR/dIDTNhycnLgdOSmt5Nj9OrqaqvVarVa27RpExYW5nK5EhMTwfQsIiJCJpMR7U51dfXu3bu7dOmSlZXVrl279PR0aCun0+lwOIirB2hramtrwSepoqKiffv2bdu2raioSE1NLS0ttVqtSqVSr9eDZZxUKr1y5coPP/wQHh4eEBCg0WjAYpaQEx2VL54Zbrf79OnTLb+Ri9LSUoRQTU0NtCzpGR6eOLQgXP+GYyv47Ha74cCND2BIAtCdEidOnOhBa7FYvGIg1jYOh2Ps2LE1NTUfffTR9evXxWLxhAkTBg0adOjQobKyMpPJ1K9fP4vF8uCDDxYVFe3atauqqorMKqj+dTT1orlePuDXS37sdDp37tw5b968uLi4S5cu3bTCIpEI3CbAODYyMvLChQvl5eWpqanh4eEJCQlyubyurg7cGhUKxcKFC7/44ovZs2d/+OGHXbt2LS4uvnjxIpzWwOKcWH2JRCKtVqvVahmGKS8vj4mJOXfunNVqbd++fUZGRq9evSwWC5h96/V6g8Ewfvz4pUuXjh8/fsuWLSNGjLhx40ZhYSFZMKP6eCXIKw0qQyMkCWT/2rt3b8+ePcnoKNBeDgkWOB1j/OSTTyKENBoNZDhqiV1lM6ioqGio0KIC8vhcpT+Jkt4SZTVJ6I0xViqV33zzjUQi0ev1bdq06d279++//15cXNyvXz+xWAw21gMGDFi3bl1wcDDodSA4FfS85svyyFEDSySTyQTW5DNmzLh06ZLJZIqOjubaizVV5+rqalhOh4eH5+XlKRQKi8XStm1bIKyrq0tMTDx58iSYLiQmJl6/fr1v377V1dXr1q07d+5cWVmZUqmE8wxwWQRmp9NZUVEBm9WQkBAwr4mOjtbpdEFBQeDmMmjQoJycnNOnT1+/fn3MmDF//PHHyJEjL168uG3bttLSUpfLJZfLwQBQrVYTcyLvtDfcnu3DYTFBXl5eZmYm+RfM3GiZ1zQDPnUm2LBhA5DEx8eDPRd/6HQ6gfa08MrAVK3hty1ciQAJ9H4Ii1ZXV3f69OkPPvhg9uzZubm5Fy9efOqpp2bOnBkYGBgcHLx79+7S0tIbN25gjM1mM9hwQiinlg+7cAvGGAK35ebmGo3GkpKS1NTUm3rJQhu63e709PQTJ06AZXZxcXFMTExdXd2sWbNcLldkZCTLsl9//XWnTp0CAgKKioo2bNiwc+dOu93+6KOPLl26FFwrYCgh4wjsXZ1OZ0RExPXr12E7mp+f36NHD6PRmJmZyTAMaJhPnjwZEBDgcrmuX7++atWqEydOFBcX9+zZc/fu3XA6IhKJuH3ex00Ozz7dr18/8vnYsWNnz54VSA6vXLlCPo8bN45KL7/rrrvgAy05RJy56/jx43SncXKKwJ/KZrOB3VZdXR2cLorF4nXr1h06dKioqOjDDz+sqKiA0/+rV69WVFR07drV5XLl5OTg+tiELXQIgtcEa8KzZ8+WlJRA/Tdt2oQQOn78+OTJk5tncDgccGp38uRJlUrFsmxdXV1xcXFxcbHdbj927JjBYAA3qP79+7tcrtra2qVLly5ZsoRhmAsXLrz99tvQbiKRqK6ujqvCAGaEUEFBAShCXS7X+fPnMcb5+fnXr18Hh2mHw2E2m8vKynJzc//zn/9s3ry5vLzcZrMdOHAA3i+EfuSblpjKenLYsGFgpnD58mW65jWNgvoyVbhF9e0GOI5j6gEhJJh6nymlUhkdHW00GoOCgsAnMCAgIDY2FuwBFAqFTqcj94Jis5m1pUe/ZBjmo48+Iv8ajUZyjNG2bdtm6kzOLcDWzGAwmEwm+FetVvfp0+f7778/evTo+vXrk5OTly5dKpPJOnbsGBsbK5fLExIS4DQCTiwkEonZbCbeydAaJJKNSqUyGAxwMhESEvL0008fOHDgyy+/XLduXf/+/WfOnKlQKKBx4JADDkLg8ANu93bfThMNm/tW1cSPloArh0QCZTIZ95CDZVk4jh89ejQcHsKPwSEQfgNO8SDMLS9drVar1eqEhASPdVPz6QrhTA/COkGQNZ1OZzQaif5JKpVqNJoxY8aIRKKgoCCGYcLCwsBWVq/Xa7VaGEo0Gg08FzGKgKEERgSxWCwWi1UqVVBQENHEyGQypVLZo0cPiUQCtqzBwcEMw8CoBKog0P14MPvhx00AVpQgQsReH6wrWU5UX5DMiIgIYpPJMAzoMOEHcDsYDPhQDa/u0mg05HyfrYfRaIQ6QK2MRiM8S3h4uEajgfNSlmXj4+PhwFClUoFlAsxgwAzmAVwvapZlieiqVCqGYSwWCzw1aFNBPkEmQYDVarVSqQSG/3k7+dAofvyfApwzET89El+U+xuudlQqlXL1HAQeOj9B6wxzFAStICHhiD03KGNApQwPAs5KWq2WxFkEWVIqlWA9y7IsnGfCuSL4YRBzBQAopeAv2QSCmkcmkxFnKLgul8thswqbRkFbww8//PDDDz/uNPgXqH744QXIbhnco2C1KZPJwLIPlvHEngGs/OA6sZQgLvxMfawgWPr6c2b44YcXIBpgMFLHGMPZo0dmKHBiBL0OGMTiep94ch6DMVYoFOQWvyj+H4JYLDYajXPnznW5XLm5uUlJSWDn2fwZnR9cEOUwQgiUtMQYALSsISEhCCGWZSFwI5kMtVotBPiCORD0Oi3JkOfH3wdwjKbT6aZNm/baa69VVlYajcbx48djjIWzc/q7Ag5I4QwDrBfgvB6OMeAkE6zh4MRCJBJ17do1LCwM3BTBxgCOQ0huqVv9TK2FgoIChBA4tghaEHRun2//4YcfwBaX7pmv0WjUarUikaiqqgqcjNxu99NPP40xPnHiBGjeb9y4QbFEimjKybgVzLOaApxVgjsyHBhC+g2WZQ0Gg0KhCAgIABMfOF9V1GPSpEkQrdhoNIKRQGpqKsyrvtfmjrP5EsIv3gNEfnwoIjc3d+fOnWPHjj158uSpU6fUavWePXso1g06CvjEMAwDR2ewe3E6nSEhIbetwRPDMCSlh8lkeuSRR+B0HiF04MABWqXs2rWL2zdOnTrVzI+hMeHcH4QQrHnUanVoaKhSqTQajQkJCXAmGRwcLJfLtVptcnIypNCBbBkdOnQwm816vR4s5nyvunB9mv1r+G3+wBhDLDkqotjM7T6TV1dXQzwRclj82GOPEett/nFuuJI2YcKEa9euVVVVlZaWXrt2rbS01Ol0jhkzhmcRgK+//jotLY3bzj63NowagKSkJPhQVFQEw8f58+e7d+9OfuBVbJimqtTy7gHTHYgfWPDAFa1W265dO5VKlZSUlJycrNPpQkNDhw8fnpKSsnr16sGDB0MKg/bt28vl8r1798LeEkwRWl7/xuvt+/1NQKfTOZ3OgQMHkisUg/xCnXlukWEtwc1p58HvbbPAJh7/FTNmzEhISDh+/HhMTMyZM2d4NjW86b1797pcLoVC4Xa7jx8/7nK5kpOTXS7X9OnT+ZADIJEbSWpNQLT2fMCybFBQkFQqtVqtLpcrLy/vpZdeuvfeex988EH+NQdw3x1XyBsFyA9MhmKxGPI0dunSJS4uLjo6euLEiTt27FizZs1HH31UWFj422+/zZgx48CBAx07dty/f/977723cOHCb775pk+fPpD/lEzyfOtNC0S3S5GTC/51/u23325KjpuNV33TewFgomWz2SDgNAHPxQKsgkiKLpPJFB4eztQ7knORnp7uLXlMTAzGGJzrgRPqzN8ZmmXZqVOnkn/79et3+fLlLl268KT1gFeNDEKo0+lgUZqVlRUZGRkQELBixYotW7Zs2rTpvffeO3HixJtvvjlgwIBZs2Y99thjGzZsOH/+fFZW1tq1aw8dOhQbGxsUFLRt2zYwcPV9tBJCFBFCEOS8+WW6zxCozh7k3vJDMCLuZGKz2cDoecOGDXv27PEQxUOHDnk41LRcPsViscViSUtLW7NmDar36D9x4gSXwbc+QWrIdX0C9yiM8b///W8fOAnAMxAqabPZ7rrrLpZl4Syu0XHEN3j1BiEGHCwvJ06cCG4cGo3m7rvvLigoePjhh2fMmPHWW2/l5OQcPnz4wIEDb7zxxpgxY954440XX3xxwoQJp06d2r59e2xsLMQOh8zhfOvt4/2NAWpz9erVn376iSItwGg03p6iCPdC8DKMMdcdlmEYCElGpeYMw9jt9v379xcUFOTl5blcLvAG4L8th4zWuMFyoLy8HEYWn5llMllWVhbLsrAQZVn27NmzJ06cUKvVPOvcEF41MqxLIV2pwWDQ6XRqtXrx4sUffvjhAw888NJLL73xxhvl5eWLFi06evTokSNHTCbTXXfd1blz53Xr1v3666+LFy9OTU0FnTYkuiP5L4Stt7fghlqiBVod+qb8vt2LEHr11Vc93NJBQUeYyRG8b6XAMq+wsNBoNIIH3aBBg+Ar7tzig4qo0Wen0uCzZ89WKBTPPPMMQgg8sCACQIcOHchvqEQn8LaqxD1Kq9VCElW5XG4ymZYvXz5z5syVK1c++uijU6ZM2bZtG+hOJ0yYMGPGjHvuuefMmTORkZFdunSZPXt2u3btwsPDYdvpY1Qkun0atCDECw58xqgwc3HbTomoPvl2Q0+88PBwQss35gJCR44cyczMbN++vUgk2rdv3/vvv28wGGQyWUpKypkzZ3zjjIuLIzXcuXMnuc5tEN82dcuXL0cIvfvuu0x9HEedTjdv3jyXy1VcXLxgwQLfKtworl275u3rI16XGo1m6tSpMplMp9MtWbKka9euubm5gwYNWrNmTc+ePePj400mU2hoqEajefLJJ5csWTJ27NiFCxeaTCaj0ahQKEJDQ/V6PQT78LH21Ls1CCTs9V999VW6hxncuYUiLQFEc+VDzk3q4gGuL5xKpWpJ/E8PwHxiNBphW5iTk/P0008/8sgjeXl5kJls8eLFVVVVubm558+fZ1kWcj+3EKRuYDtRXl5eXFyM/wpvKwyjEhyj19TUjB07tn379tAOJM2L0WiEyADekjfzCC2/BY4fILA3nO8/9thjBoMhJSXlySef/PDDDxcuXNizZ881a9Z07twZ4i/GxcUdOXIkPz8ftouRkZFBQUEkYjLNQP1UMHPmTGCOjo6my9wKs6IQzDA2TZ061el0Xrx40YcgsdCttVrtW2+91bt37ytXrkD3LS4uLikpgZOGgoICu90OZ9xWqxXOr2/KrNVqudqmkpISojUFFBUVkfNArwAOuxKJZODAgbt27QIzazCtxhiDgoT8mNdJQD28fYMwuoEeVaFQQD4MvV7ft2/fadOmLVmyZMmSJRaLRSwWm0ymhISEESNGXLly5auvvvrhhx9UKtXjjz8O8fzFYnFQUFBL0hbQqXcLoVKpgNkjEiYVCCeKwskhwdmzZzHGLpeLpD31Cvfee++ZM2dqamouXbrEMAzkHr1y5YrdbidNjTEuLCzEGEOu0paTcyfthvChtgTPPPPMtm3bIKATIayoqCDDBHGQ51MK8ukNgqkaMXbTaDQBAQEGg2Hq1KmDBg1KT0/XarUpKSkdO3YMCQmJjIxMSEgoKir64osvEhISIHJHSEgI5OGA2DY+bneFnl4wxnPnzhWCliInQmjfvn2EfMKECT7z3HQKgtj4GOOsrCyvmEUi0erVq5944gm32z1s2LCSkpKKioqioqKrV6+CVCsUCpVKRfgxxhAFAyHUQi0lxPPNy8sjsTY2btzIp8GjoqK4/yqVyrS0tKKiIowx2M0Sc004Jt2xY4cPpQAef/xx5FN/JjF+xGKxRqM5cuRIWFiYTqeLiopKTEzMyMhISUkxGo2dOnUKDAwMCQm5fv36xYsXL126FBwcrNVqFQqFUqnk2s35aPgm9Azz1VdfcS/m5ORQoaVe527dujXPzDNYOHd2giJg7GQYBua35m+HrVRiYmLDyQo8XLt06aJUKu12e69eveA0Bfxcn3zySW+nGg+5hVL451zQaDQpKSmQLBUccLnDFmhNEL/zGN86BmhQIemLXq+Pjo6Wy+VRUVFyuTwpKen111/v1q2bVCo1GAwRERG7du369NNPV69e/fnnn8MTJSQkhIaGzp8/n0Rh9EUUhZPDNWvWCCrhdJlxfe5eWrRNzY07duwoLi4uLS3Nzs6GKy3veUuXLoXQ2g3BMIzT6bzrrrvIkSbgzTffPHHiBJ8H2bp1K1AFBwfz4QGASsPlckFke3KdOO/ytFj2rW8QzyaLxTJgwIBHHnmkY8eOCQkJeXl5ISEh7dq10+v1bdu27dy587Jly9avX9+uXbsvvvji/fffHzNmjFKp7Nu3LyRFhQhxPj6CcKL46KOPYoybz67uG6jX2aNb06IlACG/du0aKQ4+6/X6lkcfhJmqZ8+ejYoirrcrAI2I1Wp95JFHNm3a1KNHDyLzvoFisyxZskQsFp86dQpjvG3bNo9vuY5FPnRl7i7X23u5C9QOHTqo1epOnTpt3779nnvu6dq1a2hoaNeuXXft2vXOO+/odLpu3bqNGDHio48+6tChw9ixY2UyWWBgoEQiSUpKIlkffXGSotLQja5/WphwzwcINCsKJIeAtWvXwgew28YYEwP02bNnR0dHt2QNmZGRAfaNS5YsaUq/AovS6dOny2SyHj16MAwzfPhwPjUnzBAuwNvb27dvTz4zDNOhQ4dx48bh+sU5wGAwxMfHM/XRkHnW04eXCAcYsLAUiUTZ2dlms1mn0ykUiqSkpLVr1/br1y8kJCQoKCgzM3Pr1q1nz569ePHivn375HK5xWKJiIiAXSI3+6rvD+D1nTfD6dOnJRLJ999/T52Zbp25GvwjR45Q4fQA6V5isRj0IikpKaDtFIlEHTt2RC3QHBIxMJvNhYWFDX0m3n//ffiByWQCq06epwJ33XUXIaeSJDMuLm7FihX5+fkul4vrCiMSiYYPH85zAcynV0As8LCwMMiU2LZtW7VanZiYOH78+N9///2ll1566aWXVq1aNXTo0GXLlvXp02fixImPPfbYgAED0tLSFAoFnChKJBKIYsxLFH2offNgGOamSWR9hkBTIkVCLpj6xKmwNWIY5tixY7CThE2Ft0WTECxqtTosLKx9+/a4iUzJ4eHhfGrOlfNDhw7xoQLo9Xqn07l//35ysk9Aonr7TI4xhhBsPrxKsjqdPHkyRPvv06dPVlZWfHz8888/f/Xq1blz55pMpsOHD3fu3HnlypXPPfdcRkaGWq0Gp+F+/frBySQc1fjomSFQLwwPD9+7dy98pm74RrfOwLZq1SpahE3h6aefjouLYximZ8+ecAVCM/gW4r4VAKGvAfxfYkhIyCeffHLkyBE4Vt21axfiaGsYhunatSsf/vT0dJ87BsTCABumkJCQSZMm6fX6pKSkjz/+eNy4cd9+++1TTz2VmZk5ceLEZcuWDRw4EPKISiSSsWPHGgwGqVSqUCggFg5bD68r4bsJebPo2LHjbRvQoRnwP19uCuBMjOpViAKVQhcYY4jTIRKJKKrfJBLJjBkzGv2Kf5/hxgpoOYgGFf4GBgZOnjwZ1h2jR4+WyWRDhgw5fvz4pk2bpFIpBBYKCgqCAHAWiwW2muBpBX9vYZweP24ClmXLy8tvdS28Q3V1dW5u7gMPPDBv3jw+PM0I2G0yXpNgNmAoYzAYli1bZrFYIL8d5KWKi4sLDAzUarWDBg2SSqUxMTGxsbEMw6hUKp1OB7HhSMw4iqEq/BAQgYGBt7oKfvwFIEIQ9A2WLRBkEcxopk+fDhfB+2nkyJGTJk0i0gszIYkHRzJSAfNtMdL44cedAjIfsixrtVodDgckZquuroaM5Uql0mq1gvUs+FgSW3mWZcFyCNVnHZbJZJifj7Uffvjhhx9+/B3hX6D6ccsAmygwBoIPcFzhcrngEBUUyCQfKNHcQC4KyMREzl1JRtFb+ER84Fek+nHLgOsjF4NQofqcZ7DRQgiBoyb5lhim4Pr8x2KxGI7pXC6XRqOhYuhzq+AXRT9uJUDSQKUBcx1Mhmq1Wi6X9+7dOy0tze12y+VyyHQPUmoymbp37w56kdraWrvdLpPJIFP3rX6g5tC3b99bXYWWwcNHWwhERkYi4RPB+9FCkOQTLMvq9Xri9ACxegMDAyELBViKgRNmmzZt7r//frBWkUqlixYtIipN3z3iEUII+RA6qIVgGEan0wlEThksyyYnJze05Q8KCuJD63a7ibcRF7f52Pm3wU0NnYkdJjGMBpGDwDBpaWk9evQAGUtOTlar1eD2rtPpUlJSGIaJiooC4xUIaQE38qnw2LFjEUL8/Z4bxU1pb4sFKsb49OnThw8f1ul0VqsV7C0RQo0KUssB6RZIEcTsUAgDccLJ039fULTCGERaY/bs2eD128yP2fpMoBhjOPLWarUjRoxgGGbAgAHV1dUzZ85kGCYiIiI/P9/hcERGRoLf8/nz51UqldvtjoyMHDhwIOQ5BPhWbaPRmJ6efvHiRSRYxrg7wMBt//79uAHAcN63lm1U0vj4xbQCc+tAoVBMnDiRFlvzrdGSuQXmNGIFBkkIs7Ky1q1bl5eXp9frBw0a1K1bty5dusydOzc2NnbmzJn/+Mc/wM/o448/Tk1NZVn2l19+kUqlYMLiW3fftm3bhAkT/vzzTyGGqq1btzaMFeR1b2FZ9ueffw4LC/NICaRWq6urq4GOp6yTyBSA0aNHg1Wxw+FITU3lw+wB4QSGFnOjPlC06pyYmJiQkIAxLi0tHT58OPXkeQQeQ2rzPyZGZLCw/OWXX8xmc/fu3W/cuDF+/PgXX3zxl19+OXz4cO/evWtrayHxS79+/RQKxdq1a4cOHfrRRx+FhYV16tSpbdu2DMOEhYV59VAPPPDA888/Tyvpb8NtKrxQiByJ+HhTQIaTnJycqKgohmH0er1SqZRKpdyIKW63m2Si9AEikai0tPThhx/GGMfFxcFFCK4Bp0O05nQSOLjRzsFnr9/ybtcMA0LoxRdfhH9FIpHZbEb17U9LyCUSyciRIz2oBM0/UVlZedMfw+bQZDKBieaCBQsWLVr0r3/96/jx4zU1NadPn5bL5UqlctmyZcHBwbW1tWaz+cEHHxwyZMiGDRvuvfde8AlctGjRF198AXqRFnYYkFhaw1xT8t/oAuHw4cM+FsOy7KhRo8i/eXl5HsFnUQt2500hIyMD18dZIRdVKpVarW7Xrh0JPnur4kB7y7xp0yafSbRabcNRE+axq1ev8qw2dHQhnr0hSGtMmTLlpj+Wy+VwPiESiRITEw8ePMiybJs2bf7973/b7fagoCCz2WyxWN58881evXr16tVryJAhKSkp9913X1hY2P79+0tKSs6fPx8bGws5RmHh2pJKPvLII48//jjGGGwJyHWeMyS3hZsaoysqKiATgXdoSh9FyuA5axEej0jEoaGhoKrmQ96wFCFEkTsw0WVGCGGMYQ3Ch8RkMlVVVcHA7G0wVW/hbTvL5XKZTAZrLrlcvnz58piYmIqKiv/85z/Lly/X6/VSqbRHjx6bNm368ccfN2zYoNFoLBaL0WhcvHjxSy+9ZLfb58yZM2DAAKlUGhwcDK4PNy2UGzhTIB0bN4JJw2+bmlqaU/42H5G6uLiYj9qX+Ia8/fbbECdGLBa//fbbs2bNIr+hvp+hTkg0hAJtvUCTzAelpaU1NTU6nS4vLw+OVQFdunQpKiq6cuUKT36C/Px88rmFrWGz2UDtWVVVxbKszWa7fPnyF198cenSpdWrV9tsNpFI1KFDh+7du/fq1auiokIqlSYkJOj1+sLCwoKCgvnz52dkZDgcDrVaXVZW5nQ6WzKtLViwgFjY1dXVeXx7+fJl/unMyOM3ehpHbdSOj4+32+1//vknH0aIJw2AGM9lZWWVlZUY47vvvhuue+Q58wFwUCnclCgcs0cRfG5HCOXn54NPHbkO2v/NmzfzryH669bLqwqT/BMBAQFRUVEjRoyA/HMkOFqnTp3MZnNERERaWppcLmcYZtCgQVqtNi0tbfPmzQ8//HBgYCCEe4IYwS0RxdjY2NzcXK/mK69QXl5OYlvS0gk1DoZhtm7diuqDpfsM8togRrLT6SR5iBwOx3PPPUfF+kHQ1amgouhDUCkPwJBfXl7+9ddfY4xhjeN2uwsLC+fMmeN2u202G5zcjhgxwudSIHk9N8dby6sN5mwQbKJDhw6DBw+eN2+e2WxOS0vTarUWi0Wn02VmZj744IOhoaGdO3dWKBTjx4//9ttvZ86c+eyzz168ePHEiROZmZkQ9aeF54pZWVl5eXkeF0HJhDH+/PPPvWwAT/zzn/88f/68wWBoqgOfP3+eZxH/RXx8/LPPPstzkd2uXbsZM2ZUV1dDfizE6dbh4eG0gimBHkXQiUs45rq6uoyMDP48uD4dmkfGNQipRn7GZ9uP6w0nvG0NsKQBmxutVhsZGQmRtt9///3ExMSQkJB58+bJZDKFQtGvX78ZM2b84x//2LNnz9atW9u3by+VSo8cOTJw4MDly5dDMm2SqLN5lJWVNXqd+hF8o0H0QDHeKHyckRmGGT9+vEeKC68AUT1TUlKKioq+++47LnNwcPDVq1fJv4jH8pp7o8eQiTHmuSAh5LezJd2OHTsGDBjAsiwoG+Giy+USi8VKpRJSI4rFYt8yVRE0087NAE5ZIDKyWCyeP3/+li1b+vfvjxCqrq6+6667HnrooZKSknHjxg0dOnT16tUnTpz47bffSkpKtm7dev36dYfDUVVV5XA4EhMTv/zyy5KSEtyCKKxVVVUmkwly9YAZiVwut1qt58+fj4+P97kFbgE2bNgAZ0E8eWBZgupH0xs3bsCJosfIyt3heAvuJNCwfzz33HM+M3PJBTJZ5AmPXQosNBiGuXz5Mix933zzTfItwzA3btzwuSxuOycmJrb8RhIeWyqVJicn5+TkVFRUBAcHm0ym6dOnr1mzZtOmTbGxsVOnTt2+fXu/fv3q6uqqq6uvX78eEBAgkUji4uJkMplKpRo3bpxCoYDIMTctFN5Xr1699u7dC73i+++/F+49BgQECEGLMMa7du3iOYISiEQiSJpHVtW0FpMY47fffpvbReh6suG/giIz4IknnqBL2NRMNX/+/LKyMj6LjmPHjnGbwqu+QQKBisXijIyMkJCQZ555Zvr06fHx8QkJCW+88UZycnJISEjnzp2nTp06c+bM559//vTp0y6XS6VSzZ49Ozw8vHv37vHx8cOHD4fVKVhHNA8wf2EY5tKlS3Bl2LBhUHmKVoGw3HU4HGlpaeQiz+xA/8Nrr70GNaa1lwNrj+nTpx84cACu0O3czUsLT521cHKIhNyCUgfDMGfOnPGtNYhDxogRI7Kyso4dO1ZZWbljx46CgoKoqKgePXq0bdv2hRdeSEpKioiI+OWXX4YOHXr33XcfPHhw7dq1ERERTz/9dEJCQlpaGnHvuKnG8tNPPw0ODgZXYzI8+TaOELjdbrC8416USqV2u/3HH39ECJF9ATXQlUOE0LRp07hWBBqNhqQZ40/ewonLt7IEnRJbXw599i3yMCH21iuPBPaVSCSJiYmTJ09+++237Xb72bNnlUplampqUlKSUqm8dOkS/DI5OfmZZ54ZP378vn37evXqBUljIM5vS0QxISEBIZScnPzUU09hjEmwWVJ/39oBGoGb2AMJ5xMLsf7dbjefDLuAZvb0gYGBdLu4cAIj6JTY8NxZCIBmEraOfHhgowUYOHCgV/eyLAvngXK5XKPR2O32EydOgNFMly5dnnrqKaPRKJPJ5HL5sGHD+vXrN3z48KtXrx47dsxgMERFRQUGBkZGRnbo0IF4eDT1LH/++Sf33/+FHmUYcD8A+KBEbaoPnD17luRHo6abhcdzuVxFRUUIIT7+yA3r1L9/f6Ij3blzJ7RIREQE/9q3cFbkT06XuTVx9uzZrVu38lRX8GlnWKBqNBqZTJaZmbl3795du3b179//yJEjV65cGTBgQJs2bWJjY+Pj4ydNmmQymXr37h0cHNyhQ4cVK1bI5XKdTkfC+8KhSAuP1CdPnowxBn8jiNDB5z1CXyXnwPn5+aQ16KvWa2trIYTBypUrKVMjhBDySH9LsX/75dADZJzWaDRz5851u916vT4lJYUPoc+tATobiUQye/ZsmUzWrl27AQMGxMfH33///YsWLdq/f39oaKharV6zZo1MJuvWrds999wTHBysUqksFotYLH788cfJblMmk2m1Wt8eYenSpb7d2BAg4WFhYdAaNNMNde/eHR716NGj/O0hmwIZmTDGLpfr0KFDMMjxpCXmdVQqSdC7d+87VBQZhoHDoeeeew5jvH//fqVSyXNWbN4BrXmAHMJhRlJSEnhpyGSyiIiIiIiImJgYEuomJSXl4YcfhsAZLMvef//9Xbt21Wq1gYGBMpkM0r+2fFYUFAUFBfBBwNNml8uVk5NDnRbUyrjeKMS3JXtTEE5a7jg5JFCr1SzLgvqaVnfxrTVE9QBXZolEMmHCBG6GelDn9OrVSy6X6/X62NjYc+fOBQcH9+jR495774XIVJA3RiwWBwQE+Pw4dMUmKSmJItstgM1mW7t2LeYctt4Og1yj8FCX3XH49NNPb3UVEOIcZsCcBn6qERERMHvDV/CtSCQCPU2PHj3Al12r1UZHR0O+ZADPHHhVVVUUH80HeDcY8LdRvikUCoWH+6Iff1eQVMpgdgMxUUUiUUhISH5+vlKpdLlcMC7bbDY4Pzxx4oTFYrlx40Z1dTV8BRtFq9XqdrslEolwMRT98ONvC5lMJhKJ9Ho97BLhQIIkRYPAxDqdTiwWt2nThruOZRgGUv/CvEoCqN4pmWH98MMPP/zwww8//PDDDz/88MOPpnE7O/v64cffHxjjqKgoIZj5JGy4VbBarXeuocIdhMDAQD4e8K0NLEBiGcLcanZqubm5AjHTqjxYRZL4LkDLx0kKcZYYEFU5JCQEIcSy7J49e/hW946FRzodOAK9AzLYICGd9ICZa2kpUEG9evW6ceMGkKenp1OcJB944AFBh5Jly5bxuZ0YPRPXW4ZhIMjF7RltpHUAAT5mzZp13333ffbZZ926dbvVNWoZLl++LOiUSJ2ZCw8PWuh/8+fPbzlDoyb8LMt6+KxQq3ET8Gr4gADHEJDbI9dQ//79wbKfG07i9sHy5ctboTFZln3ttdcOHDhgtVqLi4vpmB941NtsNlPvGSR8MEVOJHAwqKioKJgHPEQRQCW+0LVr17icU6dO5UMrFouLi4sbXofUMbCe9Navh+SN4YpxbW2tzWbDGD/77LM+1JNhGMin4nGdZ6/79ttvuY3J9Qei25/Bdhpi6iGEZs+ejRBKSkryMXQw1GzUqFFcaW7Y4ahUnVsidQg0n7jdbrC9gqjV3DQJgIZJlFvODB+USqUHp81m8zleFth5YowrKioaftutW7eWT4nETJ/siCBWBbkCk/nrr7/ubSYz3ITKAHMcd7wiJLdjjBsdMel2aRjIkpKSSDxByFo3ePDgnj17Ik72rpiYGD4F/RdCiKIQso0Q0ul0QFtaWkqLMy8vz+l0Wq3WL774QiQSgf3xmjVroCDIPIkx7ty5s89FTJs2DSGEMQYlJ2kZ39oHZGzPnj11dXUQ7yw5OZl8BYkrhg0bhii5ukql0vLycuqvkmeva3QOxBinpKQ0lEbfFpNarRaaWiwWy+XyNm3aAA+4AYrF4nbt2lHW3AgkNkJwQnwRurRk0Qj/QuNy37RWq/U5DdaqVasIMyGExR4fLx6LxSKVSoGNywMLyKNHj/rcRWCGJMr6goICmL4qKipWrVrlc4Ubgmev40ZOgivkkblC6Ha7BwwY4C05WKhDYhKRSKRUKhmGCQgIGDJkCKpvoi5duiiVSp7pLTwhnCjSJWQYBvxuaDEzDGMwGGCJuGHDBrh48uRJhFBBQQG0SVJS0q+//vrUU0/5xs/9l9vO/EdTu93e6Fvr2LEjfCAJLfkAlgwYY+oHrfx7nUwma3R577G58IGZODojhHbv3r1w4UIyH8LfUaNGkQijNKNsCCGKgs60FEUR2MBVBy6KxWIIEQ/wuaFfffVVjyuEk5uMzWdgqnGZG/Xqhj1zTU0N3QDQiNKeCOrc6ABByCHdtbeAfSCg4Wk+JIqklS/0f4AhBDQWdJmFmBWpS7hEIvEIZwQqCoVCUVdXhykF+WIYZvHixaTm/DmvX7+OMc7KyvKgqq2tTU9PR17Ouo3Wx2w2Mwxz9epVt9s9ceJEugHqPVJT+4Bm2pC7uVAqld7mRVepVFxTB/KXTIwqlcput3fu3JmyNAoxdyGEgoKCBGImrUzlPINhmJycnDNnzsC6FPJMYowXLFhgMBicTicJBOgzPyQbojWIbN++HTWRwJhKa8OTQtvC0vTatWu3/zDNRU5ODp9Zl2EYo9EYHh7eVLigyspKpj5jmrcq5eaAMe7SpQs1Og4ghRB1kLmRe9HbZSTLsgqFokePHr169YIr+K+Ai1SGPW4MNSpR9oxGI+h1IW8UAMYmq9UaExPj8yBFxn65XA66JYSQSCQaP348/2q3GniuniCGgMVi6dKlC5xgyeVyCF0HXzkcDvhMc4SismRvhpw6J6pfUfNZL+H6zaFKpUpKSmqoFvd5Gbl27dqGZRGtL58G8VDHnzt3DmN86NAhmHJramowxufOnaurq4NoTi1nJktZhmH69u0LXY1hmNLSUrvd7na7IWaUzzVvfezYsQNjvH37dh8anGVZvV7fvXt3rVZbUlKyePHi4OBgvV4fGRlJDBU++ugjPpFmG4egctiQmUpBVJizs7MRQjqdjisndFtDJBI9++yzFJnJUT6c7LtcLmITIxaLx4wZc+PGjczMTPjWtyIYhunYseN9991XVlaWlJTkcrmqq6vhOmwdyc/4PIhAYzT6a/ZOn0vZvn078AQFBVmtVohPFxAQoFAohg8f/s4779hstmYynPoCIc7oCARlBoUKueKVioJr0vX++++TMMcAt9v9z3/+k/zY5z4HVeIy03p5ZI1kMBhIQsuhQ4eiev8MH85duDLmdrsvXbrkcDggoRrY9EAyDCr1JyZHVNg8QFr7ypUrvhXBMMyOHTv0er3dbpdKpSaTSSqVwrZQoVCUlJS43W6n07lkyRL69eZpxd88uXDMGONffvmFD0+j8yGVOrMsC0GZuaBy+vTkk0+ielWBy+XauHGjWCyGA8ZJkyYhXzPJMgwTGhrKMIzdbnc6nfn5+X/88Qf/2jaKVugYPBvcaDSeO3fOaDSazWaxWFxRUaHX6+HQv7Ky0u12l5aW0jxLRPUbfZqMHGb+Lc6dnRoy8yHHGB86dAjVr/QoyiGXgUvL/1zOI+N8165doSxQ/NbU1EgkEt8yDRMdvUgkio2NJVKN6g1QdTrdnDlzeNafQCA5RJwGB/Mj3wpyOp1kkSWVSr/66iuNRgMhITds2ECKoGxkIzSEHvx48tjt9t9//536lIgaWHtgqqkKYDEJhC+99BKxk+QJuVx+5MiRkydPJicnN7onNBgM/EsRFHfddZdWq+XzErnPm5mZOWTIkKlTp/755585OTkikahTp04ffPDBHek/LfRSBCHk8xo1OjoaLLOLioqATSQSBQUF8a9bQ3eBzZs3U28HuipNYFu4cGFubu6+ffsoMrcmuIMUf7AsO27cuPvvvx/Ve2NAwo87NUqyoKuRRl31Wg6r1Uq2i1OnTqW4VscNQIu5JfAtMQms1WNiYiB8xh0HOLzdv38/3T2Xx5AHduEU+f+L1u8lFMGyLP8NGFiTREVFUW+HiRMnvvHGGwghjHFoaChdctTAtVdoUCzr448/pkXFBbGjEIKcizvriPUOAFHNd+3aFXG8e/lj3bp13H9bJxgMpmTa2hA0DbsEgxDGyU0VhG7jhGh3PCZMmCAQ86BBgwRi9qMpNHSI8cMPP/zwww8//PDDDz9aA9zoYPDBK8sDKl5C3gIcDsGWEj6QlJrwA6ZpwI/hA/d66z+FH3cWWq+LwFmqDxpCRvis4w1LvGnpTH3malRvFY05LvzkA7n4fzlOth8tQesF8Xe73dzuSDoxiQvSVOgRKnII5D6bVsCNZIqDUyn4wLIsJKkGs0wIrEasVbluOC2EQqHo16/fM888s3PnTrfb3bFjRyChG6LCDz88hY308pv+3tuVXlRUFNwSHx8PH1atWtW7d+927drd9F6yvATDJbCsB9kTiUQymQyWrxKJRCKRiMXirKys4OBgtVotEokUCgVIJixTIU18S0pECEEC+jVr1uzdu3f06NFBQUFOp9NkMun1+pY/+K1Fq52e++EjuIIEH9q2bavVaidPnty/f3+NRsMwDK0DYoVCIZPJ1q9ff/z48UuXLu3cuVMmk/38889xcXHPPvvsb7/91nwuEbLTI9tFhmHAiAlsC1UqlV6v12g0CQkJarXaYrHI5fKMjIz27dvr9XqxWJyamgq3g6C2ZASRyWRisTgqKio3N9dqtYIrU3FxcadOnRiGuXz5MpiY8dx2toKEkNNRrtcCRRiNRupPQZyY/s4jiEfXgX8ffPDBF154YdKkSTt27LBarQ6H47ffftu4cSNCSKPReESIIWLQkuKI5LAsm56eDrJ98eJF8B7GGJeXl6vV6n79+rWEhExrMD1KJBKpVMqyrEaj0Wq1IIq9evXq1avXqFGjFi1aFBwcHBwczLKsXC4PCAhQKpUwi9608iKRSCQSgZCrVKqPPvqopqbG4XA8+uijFRUVx48fl8vlpG4taYemIJPJRCJRWFgYH5KWg3rPDggIqKuro8jM3TT985//VKvVd6o0eqWNgA1bQEDAF198sWDBgn79+nXr1s3pdDocDqvVGhcXp1AoYmNjFQpFdHQ0V/cIYeRvChCYnTt3wqpSJBJptdqoqCi1Wl1VVfX666/DfjU4OJjc0mjP5sqhVCqFEAmwdIR4QRqNJjAwMDIyskOHDqtXr77vvvtyc3OTk5Nff/11nU6nVqvFYnFAQIC36lOo/8cff+xyuSBM9euvv56enp6env7CCy+0kKR5VFVVwZC0fft2IfJvTpw4ESGEMQZ73a+//hp7H1K5mQgGV69exRgXFhb6Vr3XXnuNfPZ4L/feey+0zPLly30jb1U09CEguGkACHjyoqKigICAqVOnnjx5sri4GGMMIY/cbvfp06dNJpPFYoE9Xtu2beH9RUVFKZVKCGLdjDkf2XOC2IhEIrlcPmDAAJlMZrFY+vTpExUV5Xa7DQYD2QGiJtZ7HhtF8PGDUwqQxnbt2sXHx2dkZAwaNGjRokU6nW779u19+/b9xz/+8cQTT/Ts2TM8PDwyMjIpKQluvGnDQnH5+fkY47Nnz2KM77//fnDthUmV1nEIwzBUwlg1BCF0OBx6vR5TipfVaCk8CRv1qSfMZ8+e5UMuOLhSZ7VaPbxdnU5n8yEDiL5ULBanp6fLZLJ3333X5XIVFhba7fY//vhDr9eT3padnU28E2C3xiVpBsTB3GNTCn+TkpK2bt3aUAhNJlPD2pJdIkg1cGo0Gp1OZzKZlEplQECAwWDQ6XQymUyj0aSmpr711lsRERGxsbFpaWlarVapVAYGBrYwdjPwOxyOESNGkPgLMAO35MG9AvfFUedstBTqBVGh8tgHAXNJSQmV4BcXLlxotJ58khoh1MTLaxjrpZkpi6kPNIYQYlk2MDBQoVAUFRVt2LDBbrfDShJ6bX5+fmJiYs+ePSUSidlsVigUgwcP5vL4YOfOsmx5eTnLsufPn29JVCWuzobMkFKpVKFQZGZmms3m+Pj49u3bGwyGrKys2NhYsVis0WhGjRr12GOPZWZmwkrbZDKp1WqWZWF7dtNCjUaj0+k8dOhQdXX1li1b5HK5R+pC+MszNAM3URwfHi4albqHH34YY9yuXTtaxSUkJCQlJdGtOQF1rS/1RkYIIW6MM+7w7NuYd9ddd0VHR+fn5zscDrfb7XK5pFLp2bNnYeYxm81z5swJCQl54oknQOp2797Np/IMw/Tv37+mpqa2tlYikQQGBjacORuCqGqIwgaOKIKCgiIiIubMmRMTEzNu3LikpKTExMSOHTvqdLrRo0f/9NNPc+bM6dixY3h4OBQEPKCPaap6CKHExMTIyEiyUK+pqVGpVI3O1VRcwun2kqZmP+EEvqSkhAoh6QDc+r/99ts8abds2YIEcu5tqk3J9T59+rSQimXZhx56iGXZ//znPxUVFRjj+fPnr169mmGYxMREhmHuv//+K1eudOjQQSQSBQcHDx48GGN8/vx532rOsmxSUtL58+c/+eQTt9t948YN7hmmXC6HdBENwVXbwMkhy7IBAQGhoaGxsbGdO3fu3bt3r169+vXrJxKJdDpddHR0//79H3/88UceeSQ1NVWhUPTu3Zu7sm1+u7hhw4ZJkya569FwjMjIyFixYgUEEXvxxRd5+r8JJ4oNv6JShEdBFDmFZqYJUkuPvSzofFvyANxtG8MwY8aMAc3Bv/71r+PHj2/duvX06dNZWVlff/31tWvXDh06NG7cuPXr14OmZMqUKQihzp07tzzgNHcyATsYmHuPHDny4Ycfopbtu8jMCUIIEvXQQw/17Nlz+vTpHTt2fOyxx+Li4nQ6nV6vf/DBB8eOHbtx48affvopJiZGqVTCsYfHyWQzxU2bNu2ee+7BGNtstpUrV3p8u3XrVpfLZTKZxGLxgAEDyGLVt1MN8tZef/11H25vis2jG8CV6Oho/kV4cAphckRXFNeuXSuIVDfa1iTVjFcPAF0nPDzc7XaPHTvW5XJBbtorV6789NNPFRUV165dO378uMvlqqurc7vd1dXVLpcrNDQUzutbMhV49PjAwEDQSWKMv/7666tXr6KWmexwDyfhYHDUqFFRUVGdO3eeOHHi9OnT582bFxsb+8Ybb7z11luBgYFwTHr69OmjR48aDAaDwSCrBzfPXqNQKBRWq7Wurs7lcrlcLlI9hmFUKpVIJDp16hRXSRYWFsYnWjHdbpeQkEAIbTZbjx49jhw5AuSw+6JSyqhRoxBCcMBDhZCAqTdLhIalwul0OqnweOLFF1+EfsC9iP+KlrOJRCKVSjV37tysrCy73V5bW8vtZE6n89q1a0ql0m63Hzly5PDhwz179iwsLOzatSvMdTcVIa4bB8uye/fu/eGHHz788EOM8c6dO2GvRfp6M9G4uJoSmNYeeOCBHj16fPfdd4888sjx48efeOKJBQsWJCcnL1myZNasWV9//fXJkyfbtm2bmpqq0+mioqJA4cRw0FSdDxw4sHDhQjiF27RpU2xsLKmDRCKpqan54osv4FvQk7nd7nXr1vl80E9XFLmECoWCfHa73S+88AKVUhYtWoSE2Xrh+vj8WJgAvxhjiuPRf9PBQ/aFhuE6vSqGaEEUCkVISMhvv/3mcrmeeuqpq1evWq3WRx55JDk52WKxsCx78ODBGzduuN3un376aejQoYMHD/ZKm0+kiKl3mHC73du2bYPkzAgh2P6NHj26qTNuIj9E6dKjR48RI0Z89NFHe/fuLSws/OWXX6qrq48ePTpx4sQJEybMmjXrwIEDKSkpgYGBcXFxcNoBahsP76qGBXXs2JHkA66rqyOrYpZllUplSkqK0Wg8dOgQafCSkpLRo0e3vDU8QFcU586d63K5PJIXEHJapQghhw2Z6aciRQg1a67QIhBb0IaqP9LcLdfWcOF0Ol988cWAgIDhw4cnJCR88skn0OlhQjMajXfddde7775rsVjsdnvv3r256pbmIRKJwsPDxWKxWCw2GAxJSUlut7uurs7pdBqNRpZljUajwWDo3Lnz/fffb7FYamtrm7KxJqtTmEWXLl26bNmyESNGvPrqq8uWLdu3b9+YMWOmT59+9913JyYmjh8/Picn5/PPP58zZ05oaKhSqQTbPZAouVzejOGbQqEATTLI4bFjx1iWjY+PDwwMPHfuHEIIjFpLS0vBSsblcoF+lanP1+ct+Ivim2++2SihhyhiGiGxuRMXJMbhD8hzzmUWVM4FIkdr1qzhUwBX2QDdvdGdvUajIaVs2bJl8ODBRJnR/PS4ZMmSgICAxYsX5+XlgbamtLR02rRpZJZ75ZVXunTp0qZNm65du+p0uk6dOjVqF04mcFjTqlQqi8XSvXv39PT0mTNnZmdnZ2VlRUZGgtrm7rvvnjt37p49e0aNGqXVapOTk41GIxFF0KM2OpQoFApQwGKMy8vLQSBzc3PdbvegQYOcTudbb70VEBBgt9tJLk63233mzJmHHnrI66avB93+0ZQcnjp1ij85ZEomsFqt/DkBDz30UFlZGcYYckK2wpRLrlBm91kOiSKUbJ8anTGkUunSpUuvX7++Zs2a7777rnfv3mSWbl4UZ86cKZFI7Hb7lStXrFbrr7/+arfb33vvPfhWJpP9+eef77///r///e/4+PiCggKZTBYUFNRQTojogkQplcro6Oj4+PgVK1YMGTJk1qxZPXv27NGjR1hYWFpa2pgxY0aMGDFkyJAhQ4bMmDFDpVIFBweD2d1NNah1dXXEKBRE0W63l5SU1NbWRkdHu1yukpKS1atXk76IMVYoFCdOnPC++RFCCBIPU+x8DfcsXO0az1I8xJsiCO2BAwfoMnsUQWsmb5ydStPAC4NFqUcCMIZhBg8enJ2dDWbidXV1H330ESRzvSkYhklLS9u0aRMc0MGwR84SKysrv/vuu6CgILBuNRqNGo0GTFsb8hBABlmtVjto0KDIyMgxY8bExcUNHTq0ffv2AQEB3bp1u/fee1evXt2hQ4fIyMjExESlUgkW5ODDQYJrNCxFpVKxLBsSEgILacwx2iRdvLS0lGhr6urqPvnkk8jISJ+DjBDNim+3cyFoXi0kjBzCblA4CedCWH4qz0CMS8n0iP6ayNZut8NrLioq2rJly5kzZ5RKpVarRTebEt99913o9Eqlsrq62mazFRQUlJaWdunShWEYq9X6wQcfVFRU/PTTT/B7cP9ttFtzrW1Ylp0/fz746Uul0szMzDlz5mRkZISHhyclJY0fP37fvn2ffPLJc889N2LECKVSKZPJQAhBg9q8MXebNm1g6yISibp37w6Lao/ODVcOHjwYERERGBiIEIqPj/ep7f/3Bvns4rhvn7ocehTE5fRKddcMevTogTG22+0e1+m6Vgoois8884zPLU4ekjjdIYTg8A36K7nYuXPnZcuWkYLWrl0LSchaXkR4eHhZWRn0aZvNdvbsWciKDt4eIpFo4sSJRCvTDBtBaGio0WhUq9VBQUEZGRkLFiyYNWtWt27dNm/evGPHjg8//HDEiBH9+vVLSkqKj4/v0KEDnCWC6SnX77FhKUSiRCKR2WyOiYkpLi4m86Hdbq+oqAAGpVL5yCOPdO7cmb+lMt0uIpAoEkKP1HQ8wbKsw+FwuVwN864DaEk7UYlTYfsLpk2bRqW5uY8K/fWDDz4YN27cihUrMMYMw/z555+wtoTz7sLCQsiJRW68aWOBTXlaWtru3buDgoIYhklNTUX1pjPkZ83ngSJ7PJlMBgtUlUolk8lUKlWnTp0WLlyYlZU1f/780tLSqVOnjhgxok+fPhqNxmg0KhQKqVQaGBjIPZNo4XALW1OE0KJFizDGKpXqww8/JGIMMsk/5DuZaRFCNTU1PKkQR2yuXLnCs25c7Nmzh3+Ok4YoKiqCNMDUmbkQdnXK1sdN8pmBSAI4HICjg9FofOyxx65evep0OvPy8sC1ZPv27b///rvb7dbpdM8884xvxcHaknRf7jR4U6NqhmMMwDAMJK+USCQKhQLMUENCQoYNG/af//wnNTU1KysL3L5kMllYWBgczcMSl6um8u0p6IK8Qf4dBYZOgVanwvVjjPHbb78dEREhEL+wAGGA5ub6K/kMGPtJ7wwICPjtt98YhomPj//pp58GDx5cWloaFBSkVCovXrzosatpfmFJC0y9DwSxI1Wr1eRsQyKRBAcH6/V6vV6vUChUKlVUVBTkfBaJRCaTycPOhspsRgUY46effhoh9NZbb/FnW7x4MZgBCTgD3IEQvDWgxffu3cuTx2OhSACddeDAgQihjh07giSYzeaG80krzDBcjwr4IJPJSHwdEEi5XB4ZGRkRETF16lR4KPglBH0jEkiE2efK3CYzKhekt9GaY/24ZWjKAGXUqFHgjQF9l2KmQa86NChLmfoYU6he/MCtnqxagTM4OBiOJYjXP1cO4aLPGsvbUA494JfDRuFDs9yyNy2VSj0UytDt4Iz+FlXqv4CNMfeAq+FvmPro4PBLl8sFdyFORHAu/F3WDz/88MMPP/zwo2W43bcifvyNwV3GM/VpgliWJS6yTL1rL8NJFtQUMMYikUiI08jWQeulr/HDj0bBNRVgGAa8eEH1xRVIIpYeMkm29GKx+I5O1/V3FsVp06bBh4bn7K1wOMkfDMM8/vjj3H8pknOV2LdKT0skjXs6Amow4jwNx/FEEQ0fuIcoYKPLMMydOx/+X8TPP/9sNBqbyh53+4D0Nggec/jw4bZt21LkhzDKFRUVQpuA3RTkcAj8oclLIRlESHghj7MiOGcix7kEt/ZxWgMe9iL79++/VTVpORiGUSgUkZGRHhe/++67tLQ0hBAxQ72tXmF2djZJnHrXXXfBBA5OrjyZxWJxdHR03759i4uLoTdjjE0mU3h4OEKIZwoN7jqzhbcQ+WEb5AtSKpVKpVKn0ykUCqVSuWzZMqPRCGlIwBsbItOStJlEJvk8wp0HD7PD239hwLIs+Ci2adMmISFhy5Yts2fPZhjGYrFAKFQ+vVAsFgtxZnjq1CmMMQlHQD3yCneJDg4QLY9w2Sh8sEclGwcijRKJRC6Xy2SyyMjI1NTUlStX9u/f/4033li7dm2XLl3S0tKkUilk1JPL5VKpFAKXgVdaMy7adwRuUvXmT6uFeHK73U6x24WHh3fr1u3IkSMbN24cM2bMsGHDNm7c2LNnz2+++cZoNF69enXt2rX3338/z1JuqtzzCuXl5WAxv3v37uzs7IULF4rF4qNHjyYlJfEnl8lkNpuNe0UsFkMiRIiG7Nuw0vCuljQIV0GK6idtjHGfPn0iIiJOnTo1ffr0ioqKbt26nT9//uTJk6Wlpfn5+Xv27KmtrY2Lizty5Eh1dTXLslD//4tWeEIY4/Nn5jpDIoQYhnn11VcvXboEiz2I6jt06FC1Wn3o0KG8vLyePXuePXs2Ozv76NGjt6rOHoD+BOFhr169ShwIaFmTMwxTUlLiobJiWTYmJqaysvL69esMw0BCVR8A7e/bAhXw/vvvK5VKlUq1atWqDRs2/Pbbb0VFRZWVlfn5+efOnfvss89CQkK0Wm1YWNiLL77YqVOn4OBgqVRqMBhamGxr7NixGGMImEA2ySRS7h0pwzyr3pT20ocVDoHHa2AYpn379qNGjXI6ncXFxTNmzCBrIbDqlkgkeXl58Ar5xC/iU+eGcDgce/fuxRhfvHiRPxsBmXMYhnE4HJs2bfL4QVhY2LJlyzIyMngW5ENTMPWW9CzLDho0qEOHDv/617/efffd7Oxsm832zDPPPP7446+++qpYLJ4zZ45YLA4LC1uyZMmTTz7Zu3fvnj17gqMMRPprphTI+ID++r4aDSZKUSBbQ7aFG0IIM5++yDDMxYsXp02blp+fD5kxXS7XG2+8wdQHtoKxoG/fvmKx2LcQko3WmT+cTifpH4je+h+66apVq06fPv3KK6/cuHEjKysLvlKpVDabTalUQuaSNm3a+FyKD12Zq63RarXt27eXSqUpKSmzZ88uKipav369xWKJjo6GnOoQAykiImLNmjVTpkz5/vvvZ8+eDSrWiIgIhmEgrlfz7QChujDn7ISko77DJsa33nqLVHrZsmXU+YGZZxdkGGbmzJlut/uxxx6z2+2TJ0/+4IMPuMtXhUIxYcKEJ598kkpfp/UKiXM2xnjVqlUGg4E/J0FISEhYWBgkQThy5Ai57nQ6xWLxDz/8MGbMmDZt2lBR27T8FqKtgei1vXr1Sk9Pf/zxxz/55JOtW7cmJCRotVqIZAkLUbPZHBERMW7cuOPHj3/++edwNAVBvYivdvM1pFXzFkJA2RZ0/KDFXFRUZLVaL1y4sHHjxuPHjxMtBVmj/vzzz7D72rdvH8PPr5dia5BNS6MR+/inbVm+fHllZaWHPQoMWxAxpHv37nz4fegbDMfGgGVZyA/78MMPP/fcc5mZmSqVSqlUGo3GoKAgrVYbGBgYHh5++PDh/Pz806dP33fffZDoUiKR6HQ6poEVh6A1v/VoBVEMDg724V4ICYcQUqvVCoXi6tWrr7zyyrx581iW5SoeGYZRq9VisTghIYHPy/OoMx8GAu7SlDs6sCzrs9cY98QcY1xUVMTdqDMM89ZbbzVM5OgDfOsYpHoQJs9kMkml0oULF8bGxlosFpPJZDAYlEqlXC7v3LlzeHi4Tqe7du3ap59+Onny5FGjRvXu3ZsbmM9nCyqBurS3hF7UvnXGDMj35C0qKysRQgsXLqypqXE4HAMHDtyxY8fu3bt79+597Ngxh8MBISQwxh07diwrK8vNzQV33tvnJOrw4cMIIYZhnE4nHNVCBGu3233ixInKyspJkyZ5W1t4ZXK5/OjRo1ardcqUKSSEglgsVigUY8aMOX36NMMwPgd05Al4IkihFRQUNHLkyG3bto0dO9btdtfW1kJG6tGjR4tEon/+85/9+vVzuVwjR46Uy+Xl5eWFhYVyuTwmJgZ2m/wNUO+MWbGqqgq1So4BPgzwXoOCgmJjY7///vt777134MCBoMGH4DTt2rUjCe6Tk5MZThCqW1VnUnO3233PPfcUFhaSi1VVVUS7C6KC6x2aW47s7GyYEiFvHClu/PjxUPM1a9YgTvIsn8FnVmQYBs7rU1JSFixYMG7cuIEDB+r1+uTk5CFDhphMpi5dumzdunXz5s1nzpz597//HRoaCplqo6Oj77//fm6CMN9qDsob325vhpYu4V+om2puj1Njn8l5kgAkEsnJkycZhuncubPb7b569eqPP/4YHx/PsuykSZMQ5ziLZ0EU62wwGHB92M9BgwYhhGbNmuVwOGpra+EHLpcrJibGh+IYhnn00UchHlRtbS1syQIDAzHG69evt9lskK6DVmt4vSrj5M/LzMw0mUwJCQm9e/d+5JFH3nvvvZ49e3bq1Omrr7765ptvRo4c2bVr1/Ly8t27d3/zzTdDhgx54IEHkpKSzGZzZmYmxMjkWXmfb6eClg6x3Io2fG38cwbx7wrffPMNCJhIJNq4ceMff/yRk5MzYsSIGzdu/Pbbb3369MnPz//yyy/5R5EUAnPmzCkvL7dYLDKZbOLEiWq1+p133oFwjxKJBLLlXLhw4a233mphQ02bNq1z585KpdJgMJw9exYS4NhsNrvd7nA4HnroIYwxrAt27NgxYcIEiOXD/z16O2nDqhKcMCZOnJiZmWmxWHr16hUbG5uRkbF8+fIBAwbk5uYqlcq33357165dVqu1uLj4iSeeOHr06I8//piTk1NRUZGXl4fqV7neVpj5qyvWbY3Tp08j4bVMPMlJO0JXyMjIwBhbLJbAwMCRI0c+//zzW7duhYkRDgm0Wq3ZbEYIgSW0D5DJZLRaA9fnxMT1586TJk1yu91GoxFjDAkMR48eDcczLaetrq62WCwikWjVqlXAvHLlSoPBwLJsZGTkK6+8kpub+8ADDyQnJ2OMZ82axf8pfFigEmg0GplM1q5du+DgYIPB0Lt37yVLlvTo0WPYsGH9+/dfuXJlWlpa27Ztjx8/brVax44dq9PpunTpEhISAmGjQW3jYXF1U7jd7nPnzgnUpRsGtofo2HwhqCjyl0OWZadOnQrGqyUlJe+///7WrVvXrVv3ww8/rF69esyYMfPnzxeLxVOnTl25cuUvv/wCJ8KIh+MirdaAjZyH5Qfkx8R/xQsvvPD55597RT558uTIyMjdu3e73e6cnJyff/559OjRZWVlkF1r/fr1ZrMZyoJUkzz3zL6tn1mW1ev1Op1u+PDhw4YNCw0NXbx4cWRk5N133z1p0qTNmzfHx8e3bds2MzPz4YcfHjt27J49e5YtW6ZSqWJiYrRarU6nIzlOfFBrkWp//PHH3lb+puR0Cf9LejtPiYhzZlhRUQFxgc1m82efffb888+fOXNGJBJ17dr1nXfeqa2tDQ8Ph8RVxByMT519rjC35m63e86cObhZvPLKKz6QHz16VCKRbNu2LTg42OFwlJWVwf7Z5XIFBAQolcr09PSkpCRaqTi8bRAiPAzDwNFFZmbm119/3bFjR7PZ3LZt227dusXGxs6ePVuhUBgMhuDg4KSkpCeffHLfvn2PP/642WzWarXgT+xDIgNSZ/C98u6BW0BOl/Av1IBffvlFIHKeJNxMyyzLSqXShISEoUOHwhuCzDB1dXUbN26E39wOJ4rcORlynjYKH9T027ZtIweqYCyGMZbL5devXxeLxb/99hvLslu2bEGU9ki+iSLXLVgsFs+YMWPSpElqtVqlUoWHh48ZMyYgIECj0SiVSli1Pvjgg2fPnv3jjz/MZvPYsWPhpLFNmzZc3Y8PdX7ttdcQQrfci7pF8OgZQvAjnwYS0vQmkwk+h4WFRURE/Pjjj5DwFPJvi8Xib7/99rXXXrtw4QJkekL8YmoQR16KrcH8NQUFf19haBCj0RgaGmo2m0Ui0WeffVZWVkblTL8hfJ4V4SgiLCwsKCjIYrGEh4dnZGR069bNYrHA+X5AQECbNm3MZvO8efOSkpI+/vjjBQsWwLdqtRpsNpj63AotBziOCzh9eYkWjSJJSUnZ2dn/vUEARRPmp8JSq9WQTzcsLMxqtULS71WrVul0OqfT+dlnn02ZMqW2tjYjI6OwsLC6uhpOCBhfffOo1Ll5/PHHH+np6Tzjl3EfEGyp4XBYCJCCoEGqqqogb+RNawgfpFKpw+EQi8V6vd5utwcFBcXExAQEBKxfv/7ee++FaXzgwIEqlerPP/+srKxUq9U//vhjWFjY5cuXHQ4HiLQPyweVSsUznRZFtLQnebQ1XWB+JuDwGlQqlUQiqa2t7dix465du44ePXr33XcXFBTARqKsrOynn34aM2ZMWFjYsWPHAgMD+RhnCCqH1NEwEDsXDZ2JfQBpEK9eJewdILA6XIEla1xcnEQiKS8vv3z5sl6vHzt2bHV1tdlsPnjwoN1uz8nJiYuLKy0tvXr1KsMwNptNpVJVVFQgL4fX9u3bZ2dn3z6zohcQLkoars/4xxO5ubnkM8MwZrN5+PDhoJMQiUT79u2DbT0kXeMpSHwOlFsZDMNIJBJoB66Byy2t1H/B1qdnB7EE22CRSATGN4GBgXFxcStWrOjVq1e/fv1MJtP27dsfeeSRdu3apaeng1cUU58Ms6lELE1BoN2WHwgh9Oqrrzb1MsCzobKy0of9/a1FQkICdc7z58+j26ARmAaQyWTEphRiSYF/hlwub9u2rUKhuOeeeyQSiVgs1mq1DcO9tfyJMMaQG1jQB/w/DfIyPE578/PzDx48eCtq5EeTIPLDXaSQ+Q3iLRBXb4PBAOmcuXm7mPoIcUQDdEsfiBfumMnBj78fGE5ucxKcH3abDMceDdcH6kf1BvEen4nymacqzg8//PDDDz/88MMPP1oNt1xH5Ycffvjhhx+3K7gKW9DBEpXsiBEjbm3dbi9ApHo/7mgUFBQIyu+VzUnD82RiXQAf3nvvvf79+0P6DXKxNXGbphkEOyafAVY1HkaJLMs6HA7hbCzA81AI5tscbrd7wIABDofDw0dJo9FQz7rjUW7Lf8w9+WA4MY1gVpwwYUJeXl5lZWV1dTUENIDf+7zDhHwHt39mp1YCN4qZh2cJreN+riHBpk2bSkpKbn9pVKlUEO5NoVC0b9+eLrlw7jvEEmD48OE+3AsfwHuOZVmz2QxB/jUaTf/+/d9+++1169Zt3779lVdeASsfhmHA3ed21/d88sknpaWlrVbcd99918JfNjM18e8lzYR+4eNb2Dzo9gaGYaRS6Ysvvti+ffvNmzeLxeLt27f7RsW1/BaLxQUFBREREaQdKNbZYDCkpaWB45jT6UxNTbXb7WD+hrzP7UNscRUKhUaj2blzZ1FRUXFxcXV19cWLFxUKRf/+/SdPngxrqNtdFElzz5gx4zZclTX1bqDOAq0ZPGZd3+Bht0WibDzzzDPnzp3jXUeE6gV7y5YtEonEbrebzea0tDSe0aUwxtXV1dATCKg4ZIHb1Ouvvw7em+QNut3uAwcOJCcne2XpxrV0HTx48L59+/R6vdvthhxHdrs9ODi4srJy7NixX375Za9evUaPHj1gwABUL71eAbrB77//7u2N3pXBs9uRvKLgUE+MmMgH8kupVOrV+NrMjATRBymOGhBlDCEUEhISGhrKnRVZlu3Vq1cLeZpSD8CzE5DiKIIk0KZFuHDhQlJh/moP2NFNnjwZPDBA6rZs2eJ2u/fu3WswGFo4gnAfUCqVGo1GjUZjNpsPHTrUp0+fzz777PXXX9+5c+esWbOSk5NlMpler581a5ZCoWAYZv369b49CCSK9OHGlgL8LMkKhE9hXEvChuLtcYV/sAMqs5YHIGIFwzCTJk0CZoVCgWiof41GI0Lo5Zdfpl5nD7AsGxYW1hI33xaCYoUjIiJsNttDDz00ffp07vWQkBCMcV1dHZkSmx9NuKojmUx29uxZsVjcrVu3oqKi/Pz8qqqqurq648ePE60py7LR0dHt27f31uuKC6Ff3P+Kcblc33//PU8eSOXZUA7J5EblSRryUwHxjfTgp+gcQKXO0Jm6du3qkYcDPmzdupV/YhwAxXYODAy0WCzAU1FRAU0Kc2NUVBTG2GazaTQab+VEoVDIZDKLxXLkyJGXXnrJ7XYvW7astLS0traW4bhxgkILFHK+iSJZq/twr3eAYhYvXsyfirvBaLQU/kUIIYeN8iPa3tUUq91w/6xWq5csWWKxWKjUOSUlxWOvyIdNJpMVFRVhjN1uN3ef1rt3b9goVlRUrFy5soVsLMvCUlYqlZrN5pUrV/7+++8VFRVOp9Nms5EEOPBjCHvFMExycrLJZOLTON98843P97YUuD5uJ38e1LTIlZSU8OQnpQgtitAa1FVt/Kt9+fJllmVh2cxFVVXVyy+/PH/+/OLiYn51/B+47ZyUlOSzKigvLw/y9rhcLpvNFhsbC9ftdjtcxxhDqOVmSIgIcZ0htVoty7LV1dUVFRUul2vDhg3Lli0jv4TjRJZl4+PjyV2+aVAxx2/L23u9K4ZWGWazuSkqjLFarUY0rHhJhX3OMt8SfoZhIKovLZB3WVdX523QawAcq4JuBtW3pMlkslqtH3/88dNPP52fn19WVsZzgQo7MfxX+Mz25ptvGo1GYomBMY6IiKipqamoqIBIYgQtP8OAB4dIf5mZmRcvXnQ6nQ6Hw2q1Xr58eenSpRAXl2XZtm3b7t27d/Xq1bm5uWFhYSRToFcgsi2sHCLa+9Gm5PC+++6j+CRCjE+Q4wmYqZ8lEmZucFdvsXXrVqPR+N1333Xv3n3FihVwce7cuW63u7Ky8vLly5Rq+l9dP5WOceXKlby8PHKK43K5rl+/Dh9Ig9TU1CiVSq90Y0x9iMcVK1bYbLaCggKr1QoZHWfPnh0WFta3b98zZ86EhITI5XKJRGKz2fgkbEStkD6VegENT2y4/PynRIhfIlCLEFqZTAZzOC3U1tbm5+cjhNxut8/h2EQiUVpa2t69e//444+ysrLg4GDSm3ft2pWYmEhrBCEJPxptZ69aXiaTeew5AdXV1W63G0Q0MDDQq+oRSzeZTNanT5/XX389NzfXbDb37NlTo9FotVqIDh4UFKRUKiUSidFolEgkPKPF+jB7+1iAzz2bJAy8aRG+8TdK1Shby4touD4EOwFBhz0iNr7dDrqK3r17Dx06VKPRWCwWhBDkrB8zZgxsluhWlQuukLfc0UGj0YhEogkTJlit1nvuucftdotEomeeeYZl2fT0dGAuLS31YUXdvXv31NRUhmEkEklSUlJycjK4YqSkpMBkcO7cuRUrVoA5OMgnz2lA0L5BrYBmDEc8+HkWdNPa+sYfFRVF1ksCNTd1Zu5aKywsrKKiIiAggM8CjPlrLHNSW/jQUFHkFcRisUqlysjI4F6cP3/+f/7zn8rKSmIi0kIolUqlUgnLUYlEIpVK9+zZw1XJZGZm5uTkbNu27ejRo0ajEVJ3QeYVuueK1F6o0ILOfZ08qcBksfkK+6xlwZzETwihsLAwn+vZFL9w7cwwTEpKyp49ex5++GHEbwsQFBSEMW6Y6qO6upr7s3Xr1vlQSY8rHTt27Nat28GDByF/m1dsUqm0trZ26dKlMNNqNJq4uDjioKhWq41GY1RUVFlZ2W+//bZ//36TyQSjAP/cqULZrwrXRbhGPFQIG47W/HH8+PHevXtzaRcsWECF2QNA/u677wpBjhAqKSn5+eef9+zZw58Kqrp69WqPBm94EMXnLTAMs3v37sLCwgsXLvhwu0KhMJlMBw8ehByvw4YNi4iIkMvlgYGBixYtgh+cO3eupqYmIyNj6NChJE8jH0ESdNISih1zFja0+L/55htCCHnzbrpHbQbV1dXE8fTy5ctACykKhQAWzGYdIRQYGFhXV+d0OgWyRsAYQ1P7wO+h4SCSoFQqjx075nK5fvzxR4ZhoqOjvWWGvLFGo7Ft27anTp168sknn3vuueTkZIVCcfjwYTAbcLvdNTU1YrEYdK0tZG70MQVd1wjoBIRoV12IKREhVFxcTDirq6v55yRsFDKZDEJ3L1++nAoht6+Ag6zL5aI45eIG8I2nYZ8GeSCK0wsXLgwdOhREZcyYMS1nNplMUqm0a9eu06ZNmzp1qsvlcjgcdXV1dru9oqIC3IUvX778+uuvh4WFpaen+1Z/DwglikL0bIJHH30UIXTixAmKnOPGjaNbVZFIxF2G+Zw2vHmQbRX1PQZMOH379oUp99VXX6XLD8b9GOO2bdvS4szIyJg9ezZp8+PHjwO5V4NgeHg42P2YTKbIyMj9+/f36dPH5XIVFxfPmjXL6XRGRkaeOXNm48aNMpmMrH088ot4iytXrvA5EL4JBJxwEUJeBke4JTh16pRw4xFYxgNgAyMEJBLJuXPnrl27tmzZMurkmF7CSXBQYhjm4MGDpM2/+OIL4mPhlZBoNBoQLZZlX3755fDw8P/3//7f7t27pVIp7BgffPBBoq2lstihaE7YCKA56I7WwCbswpoqWseh2+12e6uv9wqgOgZrL1qgtbnltrBarX7yySetVqtSqVy7dq3HL31z3ielsCxLGrmhoSmfFy14PBuhRYXimOpHU4AuqNFoWj+cmW9gGCY7O7uurg4h9NNPP3l85RVVMycTMCpxZfu2DqIhNPxC6IfQIGalHjNqWloaV/YalcP/08Lphx9++OGHH3744Ycffvjhhx9++OGHH37c9uBGqUF/R5XmnXG+RAu3v01Pa+L2bw0ib2C8hjGeO3duVFQU8p9+CQfhjvgxx/ODOnnrgOIMIKg5sRCQSqUGgyEiIsJgMMAx/aJFi5RKZffu3dFtPDf60LzU4ufyB6YRBbwpZvhw48YNg8EgRBGCQgixIZ2Yuj0jRTAM43Q6y8vLBw8ebLfbExMTDx8+LJfLO3bseODAAZ7x6QkICd00Qd7e4uMCtby83LcbWxker4pufMQ7DqQ1uEFKb1s5RAhhjHU6ncVi0Wg0HTt2HDBgwLx58xBCRUVFENMEeWmM2pprAWGNwgHcFQ584G8CW1hYiAReoPLvc2BA3Ggl+dScNOYbb7xBl5kLj2g0VDgBEMMKnIy4/EuXLsV/zbzgLWJjY3/99deHH344Kyura9euEI6NZdnExESuMTfP+gu3YhdW8nEDIIQ++OADWsz8eYRgbrjhhJgLsDrw+V2+9957XGbC0LCRaalYgC0lJYUW1U3hs+fesGHDNBrNqVOnHnzwQdCdQvSnwMBAiKUfFBQEv+STL+jvI4dc8I8FIlCL0GJes2bNmjVryL8gHtwW8JaQYZi9e/eSoFVkq9ywbaG4EydOQBwgnyGEYBPU1NQ02jHMZrNXviAMw4SGhgYFBUVGRj7xxBN33313WFiYVCoFCYyMjISgplRCjJLMjfypPCBU8vNGmxghZLVa7XY7RX7+VAIxQ4wcuvwMw5CIaZ999pkHIcGcOXO4t/hQEEAsFtNqZELl8fgY4+Tk5Ea/aiFA3pKSki5cuDBlypT33nsPlKhqtZpkWVQoFGKxmGeGVoRQ//79het4wC8ELcIYsyzr8TofeOABivxCNIrPfcJbft/uRQ2cTYlikDArlUoIic0zcyPx1eZDQkCq13CObearmwKSfCxbtmzgwIG1tbWbN2+GdG6oPt6+2Wz+xz/+AdNsWFgYlbWYEH0DYyyI5sZj5EO0leB3qBxyC+J5b1Oh/jGlnAUIocceewyagn/CDG7DNgxATL46ffq0t8xRUVFisdhgMAQHB7/77ruwPyTr2zZt2rRt2xaEk6LChnr3OH78uFC9jozcQsuMQLS3rShu2rTppswY4yNHjvB/BG4kZT6AzkDyPXk4yOO/witmnU4nl8s1Gk1aWprRaExNTU1OTjYYDBaLRaFQhIaGBgcHZ2dnjxw50mAwwF6RfwBv4WZFYXsdqTrZ29ClpcgpNDMXtIL2NQTG2Ol00pobaXW+77//noRF5F4n0u5bESKRSKvVRkREzJs3TyqV9unTB5QfcrncZDLpdLrXXntNJpOB+nrKlCkka7dvEK5vkFQO1Jn/C2D/6quvBJq+6HJymW/bWbElzBhjWmm6KXYRjPGPP/7Y8KLPDQ4nFmKxODEx8YMPPhg+fDjDMN26dYuPj+/cubNWq5XL5Rs2bAgICFCpVHK53Ieg/U3Vlg9JM8xe3eLLk4wfP96Hu1oC4Tq0oDYlmMe2mWhiGn12cpFWDmb+Aaaeeuop+MAwzIABA7hftWnThg8zyWBRXl4eFBSUnp5uNBqLiooSExMLCwvj4+Orq6u/+eYblmWtVivDMA6Hg8+pDGlbIfoGXQ1ZI7gTl5HCMbdv3x4J6d9w/vx56pVvlJAWf1VVFcY4ISHBZ1vimJgYSMT95Zdf1tTUaLXa5ORkrVZrsVjS09PfeOONEydOnDlzRqFQkAjCvgH/FT7z3LQIIZiFXendcXKIEMrMzOTP3EyvJZWnaCV/Ow95crkcFKQHDx7MzMy877777r777k6dOkkkEoPB8Nlnn1mt1rKysn379sXExKD6GZ5u3jVaEFAO0Z1ZdaF7Hi0q7r9kDWk0GqlXXqCpgBat2Wx+9913TSbTp59+2qlTp5MnT44aNSouLk4ikeTk5Fy7dq2kpOT55583Go18SgkODkZ3Sn9uNCnX7Tygtj4zFTSfWY1P5RtNNooxJul7feBsBjExMfyb+syZM+R8wmKxrFu3LjExsXPnzoGBgbNnz7bb7U6n0+12UzR4FqhvwBFxy9MtewdBq44EmxU9fAXuCHTt2hXVJ4ShvhEVqDX405JA+lKp1Gg0xsbGLl269PTp019//XVQUFBWVtZXX331yiuvgAPx7XOo03wRgpC2ghxiAfJ7EmY4nrqDBFI4kPdIqzVGjhxJq2+Q84yMjIzu3btLpVKNRrNkyRKVSjV9+vSQkBD+RaBWGaAxxhEREfRzn2zdurV1pkSBRmshmO9cSCQSuq+S9GyKhMTejRi4gcENrSKKiopaYa1En19w04E7EOvWrQsLCxOCmdvUWq1WiCKICzxP3LhxAwm8ISdrUXDFuJ1jDjQE5TaZO3cufVI/msXVq1dvdRW8QOvsxsHYbfTo0YKW4ocffvjhhx9++OGHH3744YcftxH+P1gKWu4lYKq8AAAAAElFTkSuQmCC",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(os.path.join(image_prefix, \"fake_images-010.png\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2cca4fff4580e0cf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-04T06:44:25.008142Z",
     "start_time": "2024-03-04T06:44:25.002599Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(os.path.join(image_prefix, \"fake_images-300.png\"))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
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